Title EMEP Unified model Importance of observations for model evaluation Svetlana Tsyro MSC-W / EMEP...

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


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


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


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


SO4 NO3 NH4

ECPOC Min. dust+

Sea salt

PM10 PM2.5


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


PM2.5 in Russia due to (15%) Russian emissions TB contribution

Sources of PM2.5 in RF:


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)


Annual mean concentrations of PM in 2001


PM2.5 PM10

Largest in Spain

Unified model

EMEP obs

General underestimation

► EMEP: aerosol components in 2001


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


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)


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)


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)


Better PM10 results for wrong reasons

N=78

N=27

N=20

N=13

What are measured PM10 and PM2.5

made of?


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


????

Largest discrepancies:

underestimation of (OC+EC) and mineral dust


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


ug/m

3

IT01

0

10

20

30

40


ug

/m3

ND

dust

SS

OM

EC

NH4

NO3

SO4


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:


2008



NO01 PM10

0

3

6

9

12

Obs06 Mod06 Obs07 Mod07

ug/m3

NDDUClNaOMECNH4NO3SO4

NO01 PM2.5

0

3

6

9

12


ug/m3


DE44 PM10

0

5

10

15

20


ug/m3


DE44 PM2.5

0

5

10

15

20


ug/m3


IT01 PM10

-10

0

10

20

30

40


ug/m3


IT01 PM2.5

-10

0

10

20

30

40


ug/m3


Intensive measurement periods


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



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


MINERAL DUST (Si, Mg, Al, Ca, K)


Still ….MINERAL DUST


In Summary….

It’s impossible to overestimate the importance on observation data of good quality for evaluation, improvement and development of models

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Title EMEP Unified model Importance of observations for model evaluation Svetlana Tsyro MSC-W / EMEP...

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