**Model Information of Potential Use to the IPCC Lead Authors and the
AR4.**

**GISS-EH and GISS-ER**

7 April 2006

I. Model identity:

A. Institution, sponsoring agency, country

Goddard Institute for Space Studies (GISS), NASA, USA

B. Model name (and names of component atmospheric, ocean, sea ice,

etc.. models)

Two versions were submitted to the IPCC archive: GISS ModelE-H

and GISS ModelE-R (which differ only in ocean component)

Atmospheric and sea ice model:

GISS ModelE (Schmidt et al, 2005, J. Clim, accepted)

http://www.giss.nasa.gov/tools/modelE

Ocean models:

GISS-ModelE-R - Russell et al (1995; 2000)

GISS-ModelE-H - HYCOM (Bleck 2000; 2002)

C. Vintage (i.e., year that model version was first used in a

published application)

2004

D. General published references and web pages

Main web page: http://www.giss.nasa.gov/tools/modelE

AGCM description and evaluation:

Schmidt, G. A. , Reto Ruedy, James E. Hansen, Igor Aleinov, Nadine Bell,
Mike Bauer, Susanne Bauer, Brian Cairns, Vittorio Canuto, Ye Cheng,
Anthony DelGenio, Greg Faluvegi, Andrew D. Friend, Tim M. Hall,
Yongyun\~Hu, Max Kelley, Nancy Y. Kiang, Dorothy Koch, Andy A. Lacis,
Jean\~Lerner, Ken\~K.\~Lo, Ron L. Miller, Larissa Nazarenko, Valdar
Oinas, Jan\~Perlwitz, Judith Perlwitz, David Rind, Anastasia Romanou,
Gary L. Russell, Makiko\~Sato, Drew T. Shindell, Peter H. Stone, Shan
Sun, Nick Tausnev, Duane Thresher, Mao-Sung Yao

2005\. Present day atmospheric simulations using GISS ModelE: Comparison
to in-situ, satellite and reanalysis data. J. Climate, 19, 153-192.

http://www.giss.nasa.gov/tools/modelE/

Hansen et al., 2006. The Efficacy of Climate Forcings. J. Geophys. Res.
100, D18104.

http://www.giss.nasa.gov/data/simodel/efficacy/

Sun, S., R. Bleck 2006. Multi-Century Simulations with the Coupled
GISS-HYCOM

Climate Model: Control Experiments. Climate Dynamics 26, 407-428.

The Impact of the ocean component on coupled model simulations

in GISS ModelE. Romanou, A., G. A. Schmidt, L. Nazarenko,

R. Miller, Y. Hu, S. Sun and N. Tausnev. J. Climate. in preparation.

E. References that document changes over the last \~5 years (i.e.,

since the IPCC TAR) in the coupled model or its components. We

are specifically looking for references that document changes in

some aspect(s) of model performance.

As above, but also:

Del Genio, A.D., W. Kovari, M.-S. Yao and J. Jonas, 2005: Cumulus

microphysics and climate sensitivity. J. Clim., in press.

Friend, A. D. and N. Y. Kiang (2005). \"Land Surface Model

Development for the GISS GCM: Effects of Improved Canopy

Physiology on Simulated Climate.\" J. Clim. (in press)

Schmidt, G. A., C. M. Bitz, U. Mikolajewicz and

L.-B. Tremblay. 2004. Ice-ocean boundary conditions for coupled

models. Ocean Modelling, 7, 59\--74

F. IPCC model version\'s global climate sensitivity (KW-1m2) to

increase in CO2 and how it was determined (slab ocean expt.,

transient expt\--Gregory method, ±2K Cess expt., etc.)

Slab ocean sensitivity to 2xCO2: 2.7 deg C

G. Contacts (name and email addresses), as appropriate, for:

1\. coupled model

2\. atmosphere

3\. ocean

4\. sea ice

5\. land surface

6\. vegetation

7\. other?

For 1,2,4,7: Gavin Schmidt (gschmidt@giss.nasa.gov)

For 3 (ModelE-R): Gavin Schmidt (gschmidt@giss.nasa.gov) and

Gary Russell (cmglr@giss.nasa.gov)

For 4 (ModelE-H): Shan Sun (ssun@giss.nasa.gov)

For 5,6: Igor Aleinov (ialeinov@giss.nasa.gov)

For data, forcing info, etc.: Reto Ruedy (rruedy@giss.nasa.gov)

II\. Besides atmosphere, ocean, sea ice, and prescription of

land/vegetated surface, what can be included (interactively) and

was it active in the model version that produced output stored in

the PCMDI database?

A. atmospheric chemistry?

B. interactive biogeochemistry?

C. what aerosols and are indirect effects modeled?

D. dynamic vegetation?

E. ice-sheets?

Chemistry and aerosols can be interactive, but were not used in the

IPCC submissions. These submissions had specified decadally varying

chemistry and aerosol fields from previously modelled time

slices. IPCC runs included sulfates, dust, nitrates, carbonaceous

(OC and BC), and sea salt aerosols. (Also volcanic stratospheric

aerosols). Indirect effects 1 and 2 were parameterised from

interactive runs. Indirect impacts of soot on ice albedo are

parameterised based on Hansen and Nazerenko (2004). Dynamic

vegetation is not used, and ice sheets were static.

III\. List the community based projects (e.g., AMIP, C4MIP, PMIP,

PILPS, etc.) that your modeling group has participated in and

indicate if your model results from each project should carry

over to the current (IPCC) version of your model in the PCMDI

database.

No relevant submissions have yet been made. (i.e. submissions

were made with previous versions of the model that are no longer

supported). AMIP submissions have been made as part of this project,

and PMIP2 submissions will be forthcoming.

IV\. Component model characteristics (of current IPCC model version):

A. Atmosphere

1\. resolution

2\. numerical scheme/grid (advective and time-stepping schemes;

model top; vertical coordinate and number of layers above 200 hPa

and below 850 hPa)

3\. list of prognostic variables (be sure to include, as

appropriate, liquid water, chemical species, ice, etc.)

4\. name, terse descriptions, and references (journal articles,

web pages) for all major parameterizations. Include, as

appropriate, descriptions of:

a\. clouds

b\. convection

c\. boundary layer

d\. SW, LW radiation

e\. any special handling of wind and temperature at top of model

1\. 4 deg lat, 5 deg long

2\. Arakawa B-grid for momentum, quadratic upstream scheme

(Prather, 1986) for tracers. Model Top: 0.1 hPa, mixed

sigma coordinates + fixed pressure levels above 150mb. 11

levels above 200 hPa, 4 levels below 850 hPa).

3\. Potential Temp, Specific Humidity, Total Water Condensate

(either all ice or all liquid).

4\. See main documentation above (Schmidt et al, subm).

B. Ocean

1\. resolution

2\. numerical scheme/grid, including advection scheme,

time-stepping scheme, vertical coordinate, free surface or rigid

lid, virtual salt flux or freshwater flux

3\. list of prognostic variables and tracers

4\. name, terse descriptions, and references (journal articles,

web pages) for all parameterizations. Include, as appropriate,

descriptions of:

a\. eddy parameterization

b\. bottom boundary layer treatment and/or sill overflow treatment

c\. mixed-layer treatment

d\. sunlight penetration

e\. tidal mixing

f\. river mouth mixing

g\. mixing isolated seas with the ocean

h\. treatment of North Pole \"singularity\" (filtering, pole

rotation, artificial island?)

Russell Ocean:

1\. 4 deg lat, 5 deg long

2\. C grid, linear upstream scheme (Russell and Lerner, 1985)

for tracers, z\* coordinate (fixed number of levels with a

fixed proportion of mass in the column), free surface,

non-Boussinesq, mass conserving, natural boundary conditions

(i.e. freshwater fluxes).

3\. Potential Enthalpy (J), Salt (kg), Total Mass (kg),

Velocities (m/s).

4\. a. GM using Visbeck et al (1997) coefficients as implemented

by Griffies (1998)

b\. none

c\. KPP

d\. yes (to 80m)

e\. no

f\. no (river outflow is added to the one relevant ocean box)

g\. 12 sub-gridscale straits driven by local pressure

gradients.

h\. filtering

References:

Russell, G.L., J.R. Miller, and D. Rind 1995. A coupled

atmosphere-ocean model for transient climate change

studies. Atmos.-Ocean 33, 683-730.

Russell, G. L., J. R. Miller, D. Rind, R. A. Ruedy,

G. A. Schmidt, and S. Sheth. 2000. Comparison of model and observed

regional temperature changes during the past 40 years.

J. Geophys. Res., 105, 14891\--14898.

Liu, J., G. A. Schmidt, D. Martinson, D. Rind, G. Russell and

X. Yuan. 2003. Sensitivity of sea ice to physical

parameterizations in the GISS global climate

model. J. Geophys. Res., 108, 3053, doi:10.1029/2001JC001167

HYCOM ocean:

1\. Mercator projection below 60N deg, 2deg x 2deg cos(lat)

bipolar patch above 60N, 1deg at 60N to 0.5deg at N.Pole

2\. C-grid, FCT advection scheme, leapfrog time stepping,

vertical coordinate: z near the surface, isopycnic below (Arbitrary

Lagrangian-Eulerian, ALE), free surface, virtual salt

flux. diapycnal mixing coefficient: 0.002 (cm\^2/s\^2) /N.

3\. temperature, salinity, layer thickness, velocity,

isopycnal/diapynal massflux, mixed layer depth, sea surface height.

4\.

a\. Interface smoothing with a coefficient of 0.002 (m/s) x

meshsize, which is of order 100 m\^2/s.

b\. no special treatment

c\. Kraus-Turner in mixed layer.

d\. no special treatment

e\. no

f\. no

g\. no

h\. bi-polar patch projection above 60N with poles over Canada/Siberia

References:

Sun, S., and R. Bleck, 2001b: Atlantic thermohaline circulation

and its response to increasing CO\$\_2\$ in a coupled

atmosphere-ocean model. Geophys. Res. Lett., 28, 4223\--4226.

Bleck, R., 2002: An oceanic general circulation model framed in

hybrid isopycnic-Cartesian coordinates. Ocean Modelling, 4,

55\--88.

Sun, S., and J. Hansen, 2003: Climate simulations for 1951-2050 with

a coupled atmosphere-ocean model. J. Climate, 16, 2807\--2826.

C. sea ice

1\. horizontal resolution, number of layers, number of thickness

categories

2\. numerical scheme/grid, including advection scheme,

time-stepping scheme,

3\. list of prognostic variables

4\. completeness (dynamics? rheology? leads? snow treatment on sea ice)

5\. treatment of salinity in ice

6\. brine rejection treatment

7\. treatment of the North Pole \"singularity\" (filtering, pole

rotation, artificial island?)

1\. Same as atmosphere, 4 layers, 2 categories (no ice, ice)

2\. Advection uses viscous-plastic rheology (Zhang and Rothrock, 2000)

3\. ice mass, ice enthalpy, salt mass.

4\. leads have a minimum value based on ice thickness (below 5m). Snow
is

one layer on ice. Snow-ice formation is allowed.

5\. salt is carried by ice conservatively but does not have a

thermodynamic effect.

6\. brine is partially rejected at freezing, and then

exponential brine flushing with 1 month decay time.

7\. None.

D. land / ice sheets (some of the following may be omitted if

information is clearly included in cited references.

1\. resolution (tiling?), number of layers for heat and water

2\. treatment of frozen soil and permafrost

3\. treatment of surface runoff and river routing scheme

4\. treatment of snow cover on land

5\. description of water storage model and drainage

6\. surface albedo scheme

7\. vegetation treatment (canopy?)

8\. list of prognostic variables

9\. ice sheet characteristics (How are snow cover, ice melting,

ice accumulation, ice dynamics handled? How are the heat and water

fluxes handled when the ice sheet is melting?)

1\. The land surface part of the GCM cell is split into two

fractions: vegetated surface and bare soil, each having its

own set of prognostic variables. These fractions are static,

i.e. they don\'t change during the model run. Soil model

consists of six layers of soil with the thickness of upper

soil layer being 10 cm and the total depth of the soil being

3.5 m.

2\. Depending on the amount of heat in the soil layer, part or all

water in that layer can be frozen. Frozen water doesn\'t

participate in water exchange until it has melted again. In

some areas part or all soil water is frozen all the time,

thus forming a permafrost. No special treatment of

permafrost is done besides what is obtained automatically

from heat and water balance.

3\. The incoming flux of water which reaches the soil surface

consists of fraction of precipitation not intercepted by

vegetation and snow and of meltwater flux produced by

snowpack. Fraction of this incoming flux that falls on

saturated soil is completely redirected to the surface

runoff. The contribution of the remaining flux to the

surface runoff is a function of infiltration capacity and of

precipitation fraction. It is modeled according to formula

\(5\) of (Rosenzweig et al.,1997). River routing is based on

existing river networks and transports runoff through lakes

(where they exist) to the ocean, as a function of the

lake/river level over the sill depth and a local topographic

gradient. Lakes are modelled using a two layer mass and

energy conserving scheme.

4\. GISS GCM employs a three layer snow model as described in

(Lynch-Stieglitz,1994). Snow fraction is computed according

to parametrization of (Roesch et al.,2001). The snow pack is

located between the canopy and the first soil

layer. Fraction of canopy covered by snow is a function of

snow thickness and of the vegetation masking depth.

5\. The movement of water between the layers is computed

according to Darcy\'s law. Frozen fraction of the soil doesn\'t

conduct water and the lower boundary of the bottom layer is

impermeable. Underground runoff is a function of the average

slope and of the density of sinks as described in

(Abramopoulos et al.,1988) section 2d.

6\. Albedo is computed as a weighted average of albedos of

different surface fractions. Those are eight types of

vegetations, two types of bare soil and snow fraction. Land snow

albedo is computed as described in Hansen et al. (1983) with

modifications based on soot emissions (Hansen and Nazarenko,

2004). Each vegetation type has prescribed albedo for four

seasons. Actual vegetation albedo is computed by means of

linear interpolation between the two closest points in

time. Each albedo is computed for six different wavelength

bands.

7\. The canopy is treated as a single layer which has its own

heat and water holding capacities. The distribution of eight

types of vegetation is currently prescribed and is fixed in

time. Leaf area index, root fractions and vegetation heights

have prescribed seasonal variation. Canopy conductance model

is based on actual plant physiology. Evapotranspiration is a

function of potential evaporation, canopy conductance, root

fraction and soil water availability.

Friend, A. D. and N. Y. Kiang (2005). \"Land Surface Model

Development for the GISS GCM: Effects of Improved Canopy

Physiology on Simulated Climate.\" J. Clim. (in press)

8\. Canopy: heat content, water content. Soil: heat content,

water content for each layer of soil. Snow: heat content,

water content, layer thickness for each layer of snow.

9\. Ice sheets have fixed height. If ice mass balance is

non-zero, implicit fluxes at the base of the ice are used to

match. In control runs, net implicit fluxes are exactly

balanced by an ice calving term. Ice sheets albedo is fixed at

0.8 for Greenland and Antarctica. No ice sheet dynamics is

included.

E. coupling details

1\. frequency of coupling

2\. Are heat and water conserved by coupling scheme?

3\. list of variables passed between components:

a\. atmosphere - ocean

b\. atmosphere - land

c\. land - ocean

d\. sea ice - ocean

e\. sea ice - atmosphere

4\. Flux adjustment? (heat?, water?, momentum?, annual?, monthly?).

1\. GISS-ModelE-R: full coupling every 30 minutes.

GISS-ModelE-H: full coupling every 4 hours.

2\. Yes. So is salt.

3\. a. precip (and energy of precip), evaporation,

momentum stress, solar radiation, sensible (and latent)

heat, long wave. surface pressure,u\*, SST

b\. precip (and energy of precip), evaporation,

solar radiation (diffuse and direct), sensible (and latent)

heat, long wave, wind speed, SAT, QS, maximum evaporation

possible, u\*, ground temperature.fractional snow cover, soil

moisture, snow depth.

c\. river runoff, energy of river runoff.

d\. sea ice mass, sea ice melt (+ energy of melt + salt in

melt), basal melt/formation rate (+ energy + salt), ocean mixed

layer ice formation (+energy + salt), sea ice-ocean stress, sea

surface height, SST, SSS, u\*, surface ocean velocity.

e\. precip (and energy of precip), evaporation,

solar radiation (diffuse and direct), sensible (and latent)

heat, long wave, wind speed, SAT, QS, maximum evaporation

possible, u\*, ground temperature, fractional snow cover,

meltpond fraction/depth, snow depth.

4\. None.

VI\. Simulation Details (report separately for each IPCC simulation

contributed to database at PCMDI):

A. IPCC \"experiment\" name

B. Describe method used to obtain initial conditions for each

component model

GISS-ModelE-R:

1 - A: pre-industrial control experiment: E3AoM20A - 1880 atm.conditions

B: initial conditions = final state of a preceding 200 year run;

that model started up from a series of previous models whose

combined simulation time added up to 428 years.

The initial run of that series started up from observed

atmospheric conditions (1 Dec 1977) and ground conditions

from a long series of earlier runs.

2 - A: present day control experiment - nothing submitted

3 - A: 20C3M: ensemble of 9 runs E3Af8\[a-i\]oM20A

B: E3Af8aoM20A start: 1/1/1880 = 1/1/year 6 of E3AoM20A

E3Af8boM20A start: 1/1/1880 = 1/1/year 7 of E3AoM20A

E3Af8coM20A start: 1/1/1880 = 1/1/year 8 of E3AoM20A

E3Af8doM20A start: 1/1/1880 = 1/1/year 9 of E3AoM20A

E3Af8eoM20A start: 1/1/1880 = 1/1/year 10 of E3AoM20A

E3Af8foM20A start: 1/1/1880 = 1/1/year 31 of E3AoM20A

E3Af8goM20A start: 1/1/1880 = 1/1/year 56 of E3AoM20A

E3Af8hoM20A start: 1/1/1880 = 1/1/year 81 of E3AoM20A

E3Af8ioM20A start: 1/1/1880 = 1/1/year 106 of E3AoM20A

4 - A: committed climate change experiment: E3Af8coM20A

B: extension of one of the 20C3M runs, 1/1/1880=1/1/yr 8 of E3AoM20A

5 - A: SRES A2 experiment: E3IP_A2oM20

B: start Jul 1,2003 of E3Af8coM20A, 1/1/1880=1/1/yr 8 of E3AoM20A

6 - A: SRES A1B experiment: ensemble of 5 runs E3IP_A1B\[\^f-i\]oM20

B: E3IP_A1BoM20 start: 7/1/2003 of E3Af8coM20A, 1/1/1880=1/1/yr 8 of
E3AoM20A

E3IP_A1BfoM20 start: 7/1/2003 of E3Af8foM20A, 1/1/1880=1/1/yr 31 of
E3AoM20A

E3IP_A1BgoM20 start: 7/1/2003 of E3Af8goM20A, 1/1/1880=1/1/yr 56 of
E3AoM20A

E3IP_A1BhoM20 start: 7/1/2003 of E3Af8hoM20A, 1/1/1880=1/1/yr 81 of
E3AoM20A

E3IP_A1BioM20 start: 7/1/2003 of E3Af8ioM20A, 1/1/1880=1/1/yr 106 of
E3AoM20A

7 - A: SRES B1 experiment: E3IP_B1oM20

B: start 7/1/2003 of E3Af8coM20A, 1/1/1880=1/1/yr 8 of E3AoM20A

8 - A: 1%/year CO2 increase experiment (to doubling): E3Ato2CO2oM20

B: start 1/1/yr 81 of E3AoM20A (first 69.5 years run as E3Ato4CO2oM20,
deviating from that run starting 7/1/year 70)

9 - A: 1%/year CO2 increase experiment (to quadrupling): E3Ato4CO2oM20

B: start 1/1/yr 81 of E3AoM20A

10 - A: slab control experiment: E3qM20A

B: start: 1/1/year 11 of E3M20A(=run with prescribed 1876-1885

mean observed ocean)

E3M20A started with 12/1/1977 observed atmospheric initial

conditions but used mean 1880 atmospheric composition; fluxes

at the sea surface were collected over the last 10 years

and used to compute the horizontal heat transports in the ocean.

11 - A: 2xCO2 equilibrium experiment

B: start: 1/1/year 11 of E3M20A (same start as control run E3qM20A)

12 - A: AMIP simulation: ensemble of 4 runs E3OCNf8\[a-d\]M20A

While those runs simulated years 1880-present, only years

1979-present were submitted.

B: start: at 1/1/1880 from 1/1/year 51 of a run with prescribed

1876-1885 mean observed ocean data. The initial atmospheric

temperatures were randomly perturbed by amounts \< 1C for the 3

runs E3OCNf8\[b-d\]M20A.

GISS-ModelE-H:

1 - A: Pre-industrial control:

B: No spin up, starts from observed state (Levitus).

2 - A: 20C3M

B: 5 runs start at yrs 120,130,140,150,160 of the PI control run

3 - A: 1% CO2 up to doubling

B: starts at year 110 of the PI control run.

C. For pre-industrial and present-day control runs, describe

radiative forcing agents (e.g., non-anthropogenic aerosols, solar

variability) present. Provide references or web pages containing

further information as to the distribution and temporal changes in

these agents.

CO2, CH4, N2O, CFCs, O3, dust, sulfate, nitrate, cabonaceous (OC

+BC), sea salt, volcanic stratospheric aerosols, solar (spectral).

http://www.giss.nasa.gov/tools/modelE/

http://www.giss.nasa.gov/data/simodel/

D. For perturbation runs, describe radiative forcing agents (e.g.,

which greenhouse gases, which aerosols, ozone, land surface

changes, etc.) present. Provide references or web pages

containing further information as to the distribution and

temporal changes in these agents.

CO2, CH4, N2O, CFCs: as observed (to 2001, then as scenario)

O3 strat: observed trend

(**Note:** the above submissions had an error in the implementation of
the Randel and Wu trend in stratospheric ozone. This lead to an
underestimate of the changes from 1979 to 1997 by a factor of 5/9.
Updated runs using a correct ozone trend will be forthcoming, but
preliminary tests indicate that only the lower stratospheric temperature
trends are much affected. Surface radiative forcing is very similar
(leading to about 0.01C difference in the 120 year change, less than the
run to run variability) ).

O3 trop: modelled in decadal time slices as function of emissions of

precursors.

sulfate, nitrate, cabonaceous (OC+BC): modelled as function of

emissions, indirect effects based on Menon et al (2002), Hansen

and Nazarenko (2004).

volcanic stratospheric aerosols (Sato and Hansen)

solar (spectral) (Lean 2002)

land use (Foley and Ramankutty, 1999)

http://www.giss.nasa.gov/tools/modelE/

http://www.giss.nasa.gov/data/simodel/