CMAS Conference 2011

transcript

Comparative analysis of CMAQ simulations of a particulate matter episode over Germany

Chapel Hill, October 26, 2011

V. Matthias, A. Aulinger, M. Quante, C. Chemel, J. L. Perez, R. San Jose, R. Sokhi

Volker Matthias, Oct 26, 2011 2

Case study on PM10 (Feb/March 2003)

Stern et al., Atm. Env. 42, 4567-4588 (2008)

PM10 daily mean concentration (µg/m3) over Germany on March 2, 2003

COST 728 study

COST 728:

European cooperation,

participants from more than

20 nations

„Enhancing Mesoscale

Meteorological Modelling

Capabilities for Air Pollution

and Dispersion Applications“

Group Met-Model CTM

IfK, HZG MM5 CMAQ

Uni Hertfordshire (UH) WRF CMAQ

TU Madrid (UPM) MM5 CMAQ

CMAQ model intercomparison

What are the differences?

Preparation of the emissions Meteorological fields Inital and boundary conditions Grids (horizontal and vertical structure) Computing platforms People who run the model

Concept

Round 1:

All groups provide input files (IC,BC,EMIS,METEO,GRIDDESC)

All groups use common CMAQ version (4.7) and chemistry mechanism (cb05_ae_aq)

All groups recalculate results of others

Expected results:

Determination of simple (model user) errors (switches …) Quantification of computing errors (compiler, platform, …)

Recalculations: Sulfate

Westerland

Melpitz

Westerland

Melpitz

Recalculations: Nitrate

Outcome: Recalculations

CMAQ model results can be reproduced by other groups on

different computing platforms

Depending on species, some differences exist but they are much

smaller than the differences in the “blind“ runs

Concept (2)

Round 2:

Agree on common grid

Use same initinal and boundary conditions (IC & BC)

Expected Results

• influence of emissions• influence of meteorological fields

Impact of emissions

Reconstruction of UH runEmission files from UPM

Sulfate Nitrate

Emissions in Central Europe (spatial average)

UHUPMHZG

Time series from CMAQ input files

Outcome: Emissions

Emissions may be prepared in different ways concerning their

temporal and spatial variation.

For short time series at certain grid points this may lead to

significant differences in particle concentrations.

Impact of meteorology

MM5 from UPMMM5 from HZGWRF from UHS

Sulfate Nitrate

Outcome: Meteorology

Numerous meteorological parameters may influence particle

concentrations.

The quality of the CTM results may not be judged from the

quality of the meteorological fields.

Open questions

Do we see „typical“ differences between „correct“ model runs or

were there important errrors in the input data?

Sensitivity study:

Annual runs (year 2000) with CMAQ 4.6 with different Boundary conditions Emission files Meteorological data

Quantify the variability of the hourly and daily concentrations at Melpitz

Boundary conditions

BC from global models: Mozart and TM4

SO2 SO4 NO3

Emissions

SMOKE-EU emissions and EMEP emissions

Additional comparisons to other emission data sets with similar results

SO2 SO4 NO3

Meteorology

MM5 (FDDA with NCEP) and CCLM (Spectral nudging with NCEP)Hourly values

SO2 SO4 NO3

CMAQ intercomparison: different emissions

Nitrate values with UH emissions lower than it could be expected.

SO4 NO3

CMAQ intercomparison: different meteorological fields

Sulfate values with UPM and HZG meteo within expected range

Nitrate values low, but may be explained by variability due to meteo input

SO4NO3

Summary

CMAQ intercomparison within COST 728 showed:

Simulations are reproducable by other groups on other computing platforms Emission data may be prepared in very different ways Largest influence on simulation results comes from meteorology Unreliable results may be detected by comparisons to sensitivity runs

AcknowledgementsEmission data has been prepared by Johannes Bieser

Most CMAQ sensitivity runs were set up by Johannes Bieser

Total gridded emissions were provided by TNO, IER and EMEP

Boundary conditions were provided by the RETRO project (TM4) and Ulrike Niemeier (Mozart)

Thank you

CMAS Conference 2011

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