an#aerocom# intercomparison#exercise ...2013/11/06 · an#aerocom# intercomparison#exercise#...
TRANSCRIPT
An AeroCom intercomparison exercise on state-‐of-‐the-‐art organic aerosol global modeling Kostas Tsigaridis, Nikos Daskalakis, Maria Kanakidou
+68 more authors from 44 ins<tu<ons with 31 models and >1000 measurement loca<ons
AeroCom aim • Document global organic aerosol modeling • Quan<fy robustness of model parameteriza<ons
• Why models differ • Why models are the same
• How do models compare with measurements • How we can use measurements to improve models
Modeling aim We need a finite number of tracers to model:
• Chemical/physical proper<es • Size distribu<on • Op<cal proper<es (direct effect) • CCN (indirect effect)
• Time evolu<on
OA within AEROCOM
0%10%20%30%40%50%60%70%80%90%100%
ARQM
DLR
GISS
GOCA
RT KYU
LOA
LSCE
MATCH
MOZG
NMPI_H
AMPN
NL
TM5_B
UIO_C
TMUIO_G
CMUL
AQ UMI
Fraction of global aerosol composition
H2OPOMBCSO4SSDUST 0%
10%20%30%40%50%60%70%80%90%100%
ARQM
DLR
GISS
GOCA
RT KYU
LOA
LSCE
MATCH
MOZG
NMPI_H
AMPN
NL
TM5_B
UIO_C
TMUIO_G
CMUL
AQ UMI
Fraction of global fine dry aerosol composition
POMBCSO4
Modified from Textor et al., 2006
31 models
# of OA tracers : 1 -‐ 62 # of SOA tracers: 1 -‐ 22
Mod
els
BCC
CAM4-‐Oslo
CAM5-‐MAM3
CAnAM-‐PAM
ECHAM5-‐SALSA
ECMWF-‐GEMS
EMAC
GEOS-‐Chem
GEOS-‐Chem-‐APM
GISS-‐CMU (II’) VBS
TOMAS
GISS
modelE-‐G
modelE-‐I
MATRIX
GLOMAP bin
mode GMI
GOCART
HadGEM2
IMAGES
MPIHAM-‐v2
OsloCTM2
SPRINTARS
TM4-‐ECPL
C
F
FNP TM5
• Year(s) simulated: Mostly 2006, but not always with 2006 emissions and/or meteorology. • Focus on surface concentra<ons, at least for now. • Emissions vary greatly. This is not necessarily bad. • OM/OC is 1.4 for most models, but not all.
Models
Source apporAonment
trSOC
ntrSOC
tPOC mPOC MSA
POA: tPOC + mPOC SOA: trSOC + ntrSOC + MSA
Median model
Budgets
Measurements
Finokalia, Greece (Koulouri et al., AE, 2008)
%
Finokalia, Greece (Pikridas et al., ACP, 2010)
hip://<nyurl.com/ams-‐database
Most models have fixed OA/OC ratios, while few of them explicitly account for changes in OA/OC during oxidation and transport.
StaAsAcs -‐ urban
StaAsAcs -‐ remote
Source apporAonment
trSOC
ntrSOC
tPOC mPOC MSA
POA: tPOC + mPOC SOA: trSOC + ntrSOC + MSA
VerAcal distribuAon
Conclusions • Diversity increased since AeroCom phase I.
• Missing OA source can be either anthropogenic or biogenic.
• OA/OC assump<on affects model skill; OA appears to be beier compared with measurements.
• More data are needed, spa<al coverage s<ll poor.
• OA/OC is our next study.
• Sta<on-‐by-‐sta<on comparison has a lot of informa<on that waits to be exploited. The same is true for the ver<cal distribu<on.