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Note:
More information on some input arrays can be found when moving the cursor above the corresponding field in the questionnaire. Those fields are also explained in the glossary.

GMI: Global Model Initiative

General information

Model name and version

short nameGMI
full nameGlobal Model Initiative
revision
dateversion : v02a
last change

Responsible for this information

nameHuisheng Bian
instituteUMBC/GEST, NASA/GSFC, mail stop 613.3
address
zip20771
cityGreenbelt
countryUSA
phone1-301-614-5092
fax1-301-614-5904
e-mailbian(belongs-to)hyperion.gsfc.nasa.gov

Additional information on the model

Contact person for model code

same as person above
nameHuisheng Bian
instituteUMBC/GEST, NASA/GSFC, mail stop 613.3
divisions
street
zip20771
cityGreenbelt
countryUSA
phone1-301-614-5092
emailbian(belongs-to)hyperion.gsfc.nasa.gov
fax1-301-614-5904

Model developer and model user

developer and userGMI team

Level of Knowledge needed to operate model

basic
intermediate
advanced
remarks

Model use at your institution

operational
for research
other use

Model code available?

is available?yes
more detailsneed permission

Minimum computer resources required

type
time needed for run
storage

Further information

documentation
model referencesXiaohong Liu, Joyce E. Penner, Bigyani Das, et al. (2006), Uncertainties in global aerosol simulations: Assessment using three meteorological datasets, J. Geophys. Res., submitted. Strahan, S.E., B.N. Duncan and P. Hoor, Observationally derived transport diagnostics for the lowermost stratosphere and their application to the GMI chemistry transport model, Atmos. Chem. Phys. Disc., 7, 1449-1477, 2007. Considine, D.B., D.J. Bergmann, and H. Liu, Sensitivity of Global Modeling Initiative chemistry and transport model simulations of radon-222 and lead-210 to input meteorological data, Atmos. Chem. Phys., 5, 3389-3406, 2005.
webpagehttps://gmi.gsfc.nasa.gov/gmi.html
additional information

Model properties

Model type

2D
3D
meteorology
chemistry & transport

Model scale

microscale
mesoscale
macroscale
short term
long term

Meteorological variables

Input data
u
v
w
ζ
pv
T
θ
θl
p
Gph
ρ
qv
qt
qlc
qf
qsc
qlr
qsh
qsg
qss
N
E
ε
K
zi
other variables i
other variables ii
other variables iii

Chemical substances

PrognosticDiagnosticDry depositionWet depositionInput data
SO2
NO
NO2
NOX
NH3
HNO3
O3
CH4
DMS
H2O2
VOC
C6H6
HCHO
CO
CO2
POP
PM 10
PM 2.5
PPM10
PM 0.1
PM 1
NH4
SO4
dust
sea salt
BC
POM
SOA
NO3
Other gases
1st radioactivity
2nd radioactivity
3rd radioactivity
Cd
Pb
other heavymetals
pesticides
1st radioactivity
2nd radioactivity
3rd radioactivity
remarks

Approximations

Boussinesq
anelastic
hydrostatic
flat earth
remarks

Parametrizations

Chemistry & transport

photolysis ratecalculate photolysis rate online using fast-JX algorithm (Bian et al., 2002, Wild et al., 2000)
dry depositionFor gaseous species, GMI uses a package developed at Harvard University based on the work of Jacob and Wofsy [1990], Wesely [1989] and Walcek et al. [1986]. For aerosols, GMI uses a surface resistance scheme based on Zhang et al. [2001].
wet depositionconsidering three wet scavenging processes: updraft cloud scavenging, rainout and washout from large-scale cloud, and rainout and washout from convective cloud
remarks

Chemical reactions

Gas & wet phase chemistry

chemical transformations calculated
chemical transformations neglected
other
gas phase chemistry (give details)DMS+OH, SO2+OH
wet phase chemistry (give details)SO2+H2O2, SO2+O3
more information

Aerosol chemistry

passive aerosol
dry aerosol
wet aerosol
sectional approach
modal approach
other
nucleation
coagulation
condensation
aerosol mixing
aerosol ageing
primary aerosol formation
aerosol-gas phase interactions
optical properties
give details

Initialization & boundary treatment

Initialization

chemistry & transport
meteorology

Input data (name sources for data, e.g. website)

orographyGEOS-4
land useGEOS-4
obstacles
vegetation
meteorologyNASA's GEOS-4 assimilation system
concentrations
emissions
remarks

Data assimilation

Chemistry & transport
nudging technique
adjoint model
3D-VAR
4D-VAR
OI
details

Boundary conditions

Chemistry & transport
surface
top
lateral inflow
lateral outflow

Nesting

Chemistry & transport
one way
two way
other
variables nested
nesting online
nesting offline
data exchange by array
data exchange by file
time step for data exchange
explain method
variables nested
other

Solution technique

Coordinate system and projection

Horizontal

cartesian
Lambert conformal
latitude / longitude
rotated lat. / long.

Vertical

z coordinate
surface fitted grid
pressurecoordinate
sigma coordinate
remarksHybrid in vertical

Numeric

Chemistry & transport

Grid

Arakawa A
Arakawa B
Arakawa C
Arakawa D
Arakawa E
uniform grid
nonuniform grid
Euler
Lagrange
Gauss

Time integration

explicit
split-explicit
semi-implicit
time step same as meteorology
other

Spatial discretisation

scalar quantities
additional information
other
chemistry solver

Model resolution

Chemistry & transport

HorizontalVertical
max5003000
min20050

Domain size

Chemistry & transport

HorizontalVertical
max
min

Model Validation and Application

Validation & evaluation

Used validation & evaluation methods

analytic solutions
evaluated reference dataset
model intercomparison
additional validation & evaluation efforts
remarks

Application examples

application examples1. Atmospheric aerosol research Xiaohong Liu, Joyce E. Penner, Bigyani Das, et al. (2006), Uncertainties in global aerosol simulations: Assessment using three meteorological datasets, J. Geophys. Res., accepted. 2. Stratospheric ozone recovery Considine, D.B., P.S. Connell, D.J. Bergmann, D.A. Rotman, and S.E. Strahan (2004), Sensitivity of Global Modeling Initiative model predictions of Antarctic ozone recovery to input meteorological fields, J. Geophys. Res., 109, D15301, doi:10.1029/2003JD004487. Douglass, A.R., R.S. Stolarski, S.E. Strahan, B.C. Polansky (2006), Sensitivity of Arctic ozone loss to polar stratospheric cloud volume and chlorine and bromine loading in a chemistry and transport model, Geophys. Res. Lett., 33, L17809, doi:10.10.29/2006GL026492. 3. biomass burning influence on stratospheric chemistry Duncan, B.N., S.E. Strahan, and Y. Yoshida, Model study of the cross-tropopause transport of biomass burning pollution, Atmos. Chem. Phys. Disc., submitted, 2007. 4. IPCC-Accent Van Noije, T.P.C., H.J. Eskes, F.J. Dentener, et al. (2006), Multi-model ensemble simulations of tropospheric NO2 compared with GOME retrievals for the year 2000, Atmos. Chem. Phys., 6, 2943-2979.

Participation in specific model evaluation exercises

AQMEII
List experiments (AQMEII)
Cost728
List experiments (COST728)
HTAP
List experiments (HTAP)
MEGAPOLI
List experiments (MEGAPOLI)