the PMIP models (Bonfils et al. 1998)
PMIP Documentation for YONU
Yonsei University, YONU Tr7 (4x5
Dc. Hyung-Jin Kim, Department of Astronomy and Atmospheric Sciences,Yonsei
University, 134 Sinchon-Dong Sudaemun-ku, Seoul 120-749, Korea; Phone :
+82-2-361-2688; Fax : +82-2-365-5163; email : firstname.lastname@example.org
Prof. Jeong-Woo Kim, Department of Astronomy and Atmospheric Sciences,Yonsei
University, 134 Sinchon-Dong Sudaemun-ku, Seoul 120-749, Korea; Phone :
+82-2-361-2683; Fax : +82-2-365-5163; email : email@example.com
Model Identification for PMIP
Number of days in each month: 31 28 31 30 31 30 31 31 30 31 30 31
PMIP Model Designation
YONU GCM Tr7 (4x5 L7) 1997
The model extends the vertical domain and resolution of the AMIP baseline
model YONU Tr5.1 (4x5 L5) 1994 by the inclusion of 2 more vertical levels.
The method of obtaining surface values of air temperature and moisture
for purposes of calculating surface fluxes is also changed.
The dynamical structure and numerics of the YONU model are essentially
those of the Meteorological Research Institute (MRI) model ; however, the
YONU and MRI model differ substantially in their treatment of radiation,
cloud formation, and surface processes. Some of the YONU model surface
schemes also are derived from those of the two-level Oregon State University
model (cf. Ghan et al. 1982).
The basic model dynamical structure and numerics are as described by Tokioka
et al. (1984). The radiation, cloud formation, and related physical parameterizations
are documented by Oh (1989), Oh (1996), and Oh et al. (1994). Descriptions
of some of the surface schemes are provided by Ghan et al. (1982).
Bibliography of key documents describing model characteristics : See
Model Documentation and References
Finite differences on a C-grid (cf. Arakawa and Lamb 1977), conserving
total atmospheric mass, energy, and potential enstrophy.
4 x 5 degree latitude-longitude grid.
Surface to 100 hPa (model top). For a surface pressure of 1000 hPa, the
lowest prognostic vertical level is at about 990 hPa (rather than 900 hPa
as in the baseline model), but the highest vertical level remains at 150
Finite-difference modified sigma coordinates (sigma = [P - PT]/[PS - PT],
where P and PS are atmospheric and surface pressure, respectively, and
PT is a constant 100 hPa). The vertical differencing scheme is after Tokioka
There are 7 modified sigma layers (rather than 5 as in the baseline model).
The 7 layers are centered on sigma: 0.0555, 0.222, 0.444, 0.666, 0.844,
0.944, and 0.989. For a surface pressure of 1000 hPa, 3 levels are below
800 hPa and 1 level is above 200hPa.
Computer / Operating System
For the PMIP simulation, the model was run on a Cray C90 computer using
a single processor in a UNICOS environment.
The AMIP integration required approximately 1.5 minutes of Cray C90 computational
time per simulated day.
Number of minutes of computation time per simulated day : See Computational
For the PMIP experiment, the atmosphere, soil moisture, and snow cover/depth
are initialized for 1 January 1979 from a previous model simulation.
Time Integration Scheme(s)
For integration of the dynamics each hour, the first step is by the Matsuno
scheme, and then the leapfrog scheme is applied in a sequence of eight
7.5 minute steps (cf. Tokioka et al. 1984 ). The diabatic terms (including
full radiation calculations), dissipative terms, and vertical flux convergence
of the water vapor mixing ratio are calculated hourly.
Orography is area-averaged (see Orography). A longitudinal smoothing of
the zonal pressure gradient and the zonal and meridional mass flux also
is performed (cf. Tokioka et al. 1984). The positive-definite advection
scheme of Bott (1989a, b ) is adopted to prevent generation of negative
For the PMIP simulation, the model history is written every 6 hours. Cloud
variables are written every 3 hours, daily accumulated data are written
once per day.
Primitive-equations dynamics are expressed in terms of u and v winds, temperature,
surface pressure, and specific humidity. Cloud water is also a prognostic
variable (see Cloud Formation).
Horizontal diffusion of momentum (but not of other quantities) on constant
sigma surfaces is treated by the method of Holloway and Manabe (1971) .
Stability-dependent vertical diffusion of momentum, sensible heat, and
moisture operates at all vertical levels (cf. Oh, 1989).
Gravity Wave Drag
Gravity-wave drag is not modeled.
Radiative Boundary Conditions
Solar constant (PMIP specification: 1365 W/m**2). The orbital parameters
and seasonal insolation distribution are calculated after PMIP recommendations.
Both seasonal and diurnal cycles in solar forcing are simulated.
The carbon dioxide concentration is prescribed value of 345 and 280 ppm
for control and 6ka, respectively. The daily horizontal distribution of
ozone is interpolated from prescribed monthly ozone data of Bowman (1988)
. The radiative effects of water vapor, methane, nitrous oxide, and chlorofluorocarbon
compounds CFC-11 and CFC-12 are also included, but not those of aerosols
Shortwave radiation is calculated in three intervals: 0 to 0.44 micron,
0.44 to 0.69 micron, and 0.69-3.85 microns. The first two intervals are
for the treatment of Rayleigh scattering (after Coakley et al. 1983) and
ozone and carbon dioxide absorption (after Lacis and Hansen 1974 and Fouquart
1988); the last interval (further subdivided into six subintervals) is
for water vapor absorption. Scattering and absorption by gases and cloud
droplets are modeled by a two-stream method with use of a delta-Eddington
The longwave calculations are based on a two-stream formulation with
parameterized optical depths, but with scattering neglected. Longwave absorption
is calculated in 4 intervals between 0 and 3 x 105 m-1
(one each for the carbon dioxide and ozone bands, and the other two intervals
for the line centers and wings within the water vapor bands). Absorption
calculations follow Chou (1984) and Kneizys et al. (1983) for water vapor,
Chou and Peng (1983) for carbon dioxide, Donner and Ramanathan (1980) for
methane and nitrous oxide, and Ramanathan et al.(1985) for chlorofluorocarbon
compounds CFC-11 and CFC-12.
The absorption by trace gases (methane, nitrous oxide, CFC-11 and CFC-12)
is normalized in each subinterval. Pressure-broadening effects are included
in all cases.
The cloud radiative properties are tied to the prognostic cloud water
content (see Cloud Formation). In the shortwave, the optical depth and
single-scattering albedo of cloud droplets follow parameterizations of
Stephens (1978 ) for liquid water and Starr and Cox (1985) for ice. Longwave
absorption by cloud droplets follows emissivity formulations of Stephens
(1978) for liquid-water clouds, and of Starr and Cox (1985) and Griffith
et al. (1980) for extratropical and tropical cirrus clouds, respectively.
Clouds are vertically distributed by cloud groups that make up an ensemble
of contiguous cloud layers, and that are separated from each other by at
least one layer of clear air. Following Geleyn (1977), the contiguous cloud
layers within each group overlap fully in the vertical, while the noncontiguous
cloud groups overlap randomly. Cf. Oh (1989), Oh (1996), and Oh et al.
(1994) for further details.
Penetrative convection is simulated by the scheme of Arakawa and Schubert
(1974), as implemented by Lord(1978) and Lord et al. (1982). The convective
mass fluxes are predicted from mutually interacting cumulus subensembles
which have different entrainment rates and levels of neutral buoyancy (depending
on the properties of the large-scale environment) that define the tops
of the clouds and their associated convective updrafts. In turn, the predicted
convective mass fluxes feed back on the large-scale fields of temperature
(through latent heating and compensating subsidence), moisture (through
precipitation and detrainment), and momentum (through cumulus friction).
The effects on convective cloud buoyancy of phase changes from water to
ice are accounted for, but the drying and cooling effects of convective-scale
downdrafts on the environment are not.
The mass flux for each cumulus subensemble, assumed to originate in
the planetary boundary layer (PBL), is predicted from an integral equation
that includes a positive-definite work function (defined by the tendency
of cumulus kinetic energy for the subensemble) and a negative-definite
kernel which expresses the effects of other subensembles on this work function.
The predicted cumulus mass fluxes are positive-definite optimal solutions
of this integral equation under the constraint that the rate of generation
of conditional convective instability by the large-scale environment is
balanced by the rate at which the cumulus subensembles suppress this instability
via large-scale feedbacks (cf. Lord et al.1982). The cumulus mass fluxes
are computed by the "exact direct method," which guarantees an exact solution
within roundoff errors (cf. Tokioka et al. 1984).
A moist convective adjustment process simulates midlevel convection
that originates above the planetary boundary layer. When the lapse rate
exceeds moist adiabatic and supersaturation occurs, mass is mixed such
that either the lapse rate is restored to moist adiabatic or the supersaturation
is eliminated by formation of large-scale precipitation (see Precipitation).
In addition, if the lapse rate becomes dry convectively unstable anywhere
within the model atmosphere, moisture and enthalpy are redistributed vertically.
For both stratiform and cumuloform cloud types, the liquid/ice water is
computed prognostically, and the fractional cloud coverage of each grid
box semiprognostically. The vertical transport of cloud water is neglected.
Following Sundqvist(1988), the fraction of stratiform cloud is determined
from the relative humidity, which represents prior fractional cloud cover
and liquid water content, as well as large-scale moisture convergence.
The cumuloform cloud fraction is a function of convective mass flux.
Cloud in the PBL (see Planetary Boundary Layer) is semiprognostically
computed on the basis of a cloud-topped mixed layer model (cf. Lilly 1968
and Guinn and Schubert 1989). This cloud is assumed to fill the grid box
(cloud fraction = 1), and the computed cloud liquid water content is added
to the prognostic value of cloud water if there is previous cloud formation.
Cf. Oh (1989) for further details. See also Radiation for cloud-radiative
Precipitation is by simulation of microphysical processes (autoconversion
from cloud liquid/ice water) in the prognostic stratiform and cumuloform
cloud scheme (see Cloud Formation). Precipitation from cumuloform cloud
is calculated in terms of convective mass flux, layer thickness, and cloud
water content. Both types of precipitation may evaporate on falling through
an unsaturated environment. Cf. Schlesinger et al. (1988) and Oh (1989)
for further details. See also Snow Cover.
Planetary Boundary Layer
The top of the PBL is taken to be the height of the lowest atmospheric
level (at sigma = 0.777). The PBL is assumed to be well-mixed by convection
(see Convection), and PBL cloud is simulated by a semiprognostic scheme
based on a cloud-topped mixed layer model. See also Cloud Formation, Diffusion,
and Surface Fluxes.
Raw orography, obtained from the 1 x 1-degree data of Gates and Nelson
(1975), is area-averaged over each 4 x 5-degree model grid box. For specification
of surface roughness lengths (see Surface Characteristics), the standard
deviation of the 1x1-degree orography over each grid box is also determined.
Cf. Ghan et al. (1982) for further details.
For control: PMIP datasets will be used for SSTs and sea-ice have been
prepared at PCMDI and were calculated by averaging the 10-year AMIP datasets
For 6 fix: SSTs and sea-ice prescribed at their present day value, as
in the control run.
The AMIP monthly sea ice extents are prescribed. The surface temperature
of sea ice is predicted from the surface energy balance (see Surface Fluxes)
plus heat conduction from the underlying ocean that is a function of the
ice thickness (a uniform 3m) and of the difference between the ice surface
temperature and that of the ocean below (fixed at 271.5 K). Snow is allowed
to accumulate on sea ice. When this occurs, the conduction heat flux as
well as the surface energy balance can contribute to the melting of snow
(see Snow Cover). Cf. Ghan et al. (1982) for further details.
Precipitation falls as snow if the surface air temperature is < 0 degrees
C. Snow mass is predicted from a budget that includes the rates of snowfall,
snowmelt, and sublimation. Over land, the snowmelt (which contributes to
soil moisture) is computed from the difference between the downward surface
heat fluxes and the upward heat fluxes that would occur for a ground temperature
of 0 degrees C. Melting of snow on sea ice is also affected by the conduction
heat flux from the ocean (see Sea Ice). (If the predicted ground temperature
is > 0 degrees C, melting of land ice is assumed implicitly, since the
model does not include a land ice budget.) The surface sublimation rate
is equated to the evaporative flux from snow (see Surface Fluxes) unless
all the local snow is removed in less than 1 hour; in that case, the sublimation
rate is equated to the snow-mass removal rate. Snow cover also alters the
surface albedo (see Surface Characteristics). Cf. Ghan et al. (1982) for
further details. See also Land Surface Processes.
Surface roughness is specified as in Hansen et al. (1983). Over land, the
local roughness length is the maximum of the value fitted as in Fiedler
and Panofsky (1972) from the standard deviation of the orography in each
grid box (see Orography ) and the roughness of the local vegetation (including
a zero-plane displacement for tall vegetation types--cf. Monteith 1973
). The roughness length over sea ice is a constant 4.3 x 10-4
m after estimates of Doronin(1969). Over ocean, the roughness is a function
of the surface wind speed, following Garratt (1977).
Surface albedos are specified as in Oh et al. (1994) for nine different
surface types under both snow-free and snow-covered conditions. Following
Ghan et al. (1982), the albedo range is from 0.10 to 0.58 over land, and
from 0.45 to 0.80 over ice. The albedo for the diffuse flux over oceans
is 0.07, and the direct-beam albedo depends on solar zenith angle (cf.
Briegleb et al. 1986 and Payne 1972). Following Manabe and Holloway (1975),
the snow-covered albedo is used if snow mass exceeds a critical value of
10 kg/(m2); otherwise, the surface albedo varies as the square-root
of snow mass between snow-free and snow-covered values.
Longwave emissivity is specified to be unity (blackbody emission) for
In a departure from the formulation of the baseline model, the surface
air temperature and moisture are taken to be the same as the values at
the lowest atmospheric level (modified sigma value of 0.989, or approximately
990 hPa for a surface pressure of 1000 hPa). The treatment of surface fluxes
is otherwise the same as in the baseline model. The absorbed surface solar
flux is determined from the surface albedo, and surface longwave emission
from the Planck function with constant surface emissivity of 1.0 (see Surface
The turbulent surface fluxes of momentum, sensible heat, and moisture
are parameterized as bulk aerodynamic formulae that include surface atmospheric
values of winds, temperatures, and specific humidities in addition to ground
values of the latter two variables. Following Oh and Schlesinger (1990),
the surface wind is taken as a fraction (0.7 over water and 0.8 over land
and ice) of the winds extrapolated from the lowest two atmospheric levels.
Following Ghan et al. (1982), the surface temperature and specific humidity
are obtained from a weighted mean (with respect to relative humidity) of
the dry and moist adiabatic lapse rates.
The drag and transfer coefficients in the bulk formulae depend on vertical
stability (bulk Richardson number) and surface roughness length (cf. Louis
1979), with the same transfer coefficient used for the sensible heat and
moisture fluxes (see Surface Characteristics). The surface moisture flux
also depends on an evapotranspiration efficiency beta that is a function
of the fractional soil moisture (see Land Surface Processes), but is taken
as unity over ocean, ice, and snow. Cf. Oh (1989) for further details.
Land Surface Processes
Following Priestly (1959) and Bhumralkar (1975), the average ground temperature
over the diurnal penetration depth is predicted from the net balance of
surface energy fluxes (see Surface Fluxes); the thermal conductivity, volumetric
heat capacity, and bulk heat capacity of snow, ice, and land are also taken
into account. Soil moisture is expressed as a fraction of a field capacity
that is everywhere prescribed as 0.15 m of water in a single layer (i.e.,
a "bucket" model). Fractional soil moisture is predicted from a budget
that includes the rates of precipitation and snowmelt, the surface evaporation,
and the runoff. The evapotranspiration efficiency beta over land (see Surface
Fluxes) is specified as the lesser of twice the fractional soil moisture
or unity. Runoff is given by the product of the fractional soil moisture
and the sum of precipitation and snowmelt rates. If the predicted fractional
soil moisture exceeds unity, the excess is taken as additional runoff.
Cf. Ghan et al. (1982) for further details.
Last update November 9, 1998. For further information, contact: Céline