Preliminary results of the coupling of
CLM with ICTP RegCM3
Dai Yongjiu1, Bi Xunqiang2, Filippo Goirgi2
1, Beijing Normal University, China
2, Abdus Salam ICTP, Italy
Outline
I.
Motivation
II. Common Land Model
III. Coupling CLM with RegCM3
IV. Preliminary results
Outline
I.
Motivation
II. Common Land Model
III. Coupling CLM with RegCM3
IV. Preliminary results
ICTP RegCM3
• Dynamics:
• Land Surface:
MM5 Hydrostatic (Grell et al 1994)
Non-hydrostatic (MM5 or WRF, in
progress)
• Radiation:
CCM3.6.6 (Kiehl 1996)
• Large-Scale Clouds & Precipitation:
SUBEX (Pal et al 2000)
• Cumulus convection:
Grell (1993) + FC80 Closure
Anthes-Kuo (1977)
MIT/Emanuel (1991)
Betts-Miller (1993)
• Boundary Layer:
Holtslag (1990)
• Tracers/Aerosols:
Qian et al (2001); Solmon et al (2005)
includes dusts (Zakey, in progress)
BATS1e (Dickinson et al 1993)
SUB-BATS (Giorgi et al 2003)
CLM (Dai et al 2003, Dai & Bi, in
progress)
IBIS (Foley; Winter in progress)
• Ocean Fluxes:
BATS1e (Dickinson et al 1993)
Zeng et al (1998)
Air-Sea Coupling (MITogcm, OASIS
coupler, in progress)
• Nesting:
Numerous GCM/Reanalysis Interfaces
Double nesting (one-way)
• Computations:
User-Friendly
Multiple Platforms
Parallel Code (Pu & Bi, Gao)
http://www.ictp.trieste.it/~pubregcm/RegCM3
Motivation for coupling CLM with RegCM3
BATS1e, behaves well, but ……
• There are some problems for several vegetations:
– For irrigated crop, eccentric behavior !!!
– Over ocean, in both weak and strong wind conditions, tends
to over-estimate LH flux.
• Not enough vertical resolution (one vegetation layer,
one snow layer, 3 soil layers)
• Lack the code maintenance (frozen code, no funds to
update it since 1993)
Ocean Flux Scheme
BATS vs. Zeng
• The BATS1e bulk aerodynamic
algorithm uses the MoninObukhov similarity relations
without special treatments of
convective or very stable
conditions.
– Overestimate latent heat in both
weak and strong wind conditions
• The Zeng algorithm describes all
stability conditions and includes a
gustiness velocity to account for
the additional flux induced by
boundary layer scale variability
RegCM:
RegCM:
BATS-Zeng
BATS
RegCM: Zeng
Biosphere-Atmosphere Transfer Scheme
BATS1E (Dickinson et al 1993)
• One canopy layer
– Stomatal conductance
(Jarvis-type) model
• 20 vegetation types
• One snow layer
• 3 soil layers(10cm, 1~2m, 3m)
– Soil T: Force-restore
– Soil moisture:
Diffusive/gravitational
Motivation for coupling CLM with RegCM3
Common Land Model, state of the art !
• In PILPS and extensive off-line tests, CLM can get
better results than BATS1e and other LSMs (Dai, 2003);
• The coupling of CLM with CCM3, CLM behaves better
than LSM (Zeng, 2002);
• CLM has been coupled with CCSM3(CAM3), WRF, IAP
AGCM, RSM, LDAS, RegCM, ……
• High vertical resolution (one vegetation layer, up to 5
snow layers, 10 soil layers)
• Better maintenance, Free updated CLM code and doc are
at http://climate.eas.gatech.edu/dickinson
or http://www.cgd.ucar.edu/tss/clm
Outline
I.
Motivation
II. Common Land Model
III. Coupling CLM with RegCM3
IV. Preliminary results
What’s the Common Land Model ?
•Motivation:
– A general land processes model is used as a common tools in
climate and weather forecasting models.
•History:
– 1996, Concept of CLM, by R. E. Dickinson;
– 1999, Initial CLM code released by Y. Dai;
based on LSM, BATS1e, IAP94;
– 3 year model validation (off-line and coupling);
– 2002, 2 branch CLM versions are officially released.
Community Land Model 3.0, Maintained by NCAR
Common Land Model 3.0,
Maintained by Georgia Tech
CLM (1999 version) major characteristics ?
1. Enough unevenly spaced layers to adequately
represent soil temperature and soil moisture, and a
multi-layer parameterization of snow processes;
2. An explicit treatment of the mass of liquid water and
ice water and their phase change within the snow and
soil system;
3. A runoff parameterization following the TOPMODEL
concept;
4. A canopy photosynthesis-conductance model that
describes the simultaneous transfer of CO2 and water
vapor into and out of vegetation;
5. A tiled treatment of subgrid fraction of energy and
water balance.
Horizontal and vertical representation
Horizontal :
• Every surface grid cell can be
subdivided into any number of tiles.
• Energy and water balance calculations
are performed over each tile at every
time step, and each tile maintains its
own state variables.
• The tiles in a grid square respond to the
mean conditions in the overlying
atmospheric grid box, and this grid box,
in turn, responds to the area-weighted
fluxes of heat and moisture from the
tiles.
• The tiles within a grid square do not
interact with each other directly.
– Mosaic treatment
Vertical :
• one vegetation layer.
• 10 soil layers, and the thickness: 17.5,
27.6, 45.5, 75.0, 123.6, 203.8, 336.0,
553.9, 913.3, and 1137.0 mm with a
total thickness of 3430 mm.
• up to 5 snow layers (depending on
snow depth). Contrary to more usual
practice, the snow layers from top to
bottom are numbered as negative
values.
Model Reliability and Maintenance?
•
•
The model has been extensively evaluated in off-line and
coupling runs in different groups independently. Good
performance in off-line and coupling validation.
Maintenance and future development (physics
parameterization and land data development) based on the
major land model groups:
Dai at Beijing Normal University,
Common Land Model
Dickinson at Georgia Tech,
Bonan at NCAR,
Houser at GSFC/NASA,
Zeng at U. Arizona,
Yang at UT Austin,
Denning at CSU
Community Land Model
New development in Common Land Model
1) Two big leaf model for leaf temperatures, photosynthesisstomatal resistance;
2) Two-stream approximation for canopy albedo calculation
with the solution for singularity point, and the calculations
for radiation for the separated canopy (sunlit and shaded);
3) New numerical scheme of iteration for leaf temperatures
calculation;
4) New treatment for canopy interception with the
consideration of the fraction of convection and large-scale
precipitation;
5) Turbulent transfer under canopy;
New development in Common Land Model:
6) Soil thermal and hydrological processes with the
consideration of the depth to bedrock;
7) Surface runoff and sub-surface runoff;
8)
Rooting fraction and the water stress on transpiration;
9) Use a grass tile 2m height air temperature in place of an area
average for matching the routine meteorological observation;
10) Perfect energy and water balance within every time-step;
11) A slab ocean-sea ice model;
12) Albedo Parameterization Based on MODIS and LDAS data.
New development in Community Land Model:
1) Replace biome-type land cover classification
scheme with plant function type representation and
its related;
2) New methods to enable simulation of the terrestrial
carbon cycle;
3) New methods to enable simulation of dynamic
vegetation;
4) Two-stream approximation for canopy radiation
transfer;
5) River routing model.
Outline
I.
Motivation
II. Common Land Model
III. Coupling CLM with RegCM3
IV. Preliminary results
RegCM3 Modeling System Flow Chart
PreProc
Global
Terrrestrial
Data
Global 1x1
SST Data
ECMWF
ERA40
NNRP1
NNRP2
EH5OM
FVGCM
HadAMH
REGCM
……
SST
Terrain
ICBC
Main
PostProc
NetCDF output
FERRET, NCL
GrADS output
ATM.yyyymmddhh
RAD.yyyymmddhh
SRF.yyyymmddhh
CHE.yyyymmddhh
POSTPROC
POSTPROCv5d
ICBCyyyymmddhh
……
DOMAIN.INFO
Vis5D
RegCM
Main
SIGMAtoP
Terrain
Land surface characteristic field
Raw Source data:
Global, Resolution: 30sec.x 30sec.
(~ 0.925 km)
- Elevation data
USGS DEM
- Vegetation/land-use data
USGS (24 category +1)
- Soil texture data: global
FAO global + USGS US domain
2 vertical layers: 0-30 cm; 30-100cm.
USGS Land Use/Land Cover Legend
0. Ocean*
11. Deciduous Broadleaf Forest
1. Urban and Built-Up Land
12. Deciduous Needleleaf Forest
2. Dry-land Cropland and Pasture
13. Evergreen Broadleaf Forest
3. Irrigated Cropland and Pasture
14. Evergreen Needleleaf Forest
4. Mixed Dry-land / Irrigated Cropland
and Pasture
15. Mixed Forest
16. Inland Water Bodies*
5. Cropland / Grassland Mosaic
17. Herbaceous Wetland
6. Cropland/Woodland Mosaic
18. Wooded Wetland
7. Grassland
19. Barren or Sparsely Vegetated
8. Shrubland
9. Mixed Shrubland/Grassland
10. Savanna
20. Herbaceous Tundra
21. Wooded Tundra
22. Mixed Tundra
23. Bare Ground Tundra
24. Snow or Ice
1. Urban
1. Urban
7.
Grassland
16. Lake
7.
Grassland
7.
Grassland
15. Mixed
forest
19. Barren
16. Lake
90 sec x 90 sec
5 patches:
1. Urban 2/9
7. Grassland 3/9
15. Mixed forest 1/9
16. Lake 2/9
19. Barren 1/9
regroup 30 sec. data to
60, 30, 10, 5, 3, 2 min. data
16-category Soil categories
1. Sand
2. Loamy Sand
3. Sandy Loam
4. Silt Loam
5. Silt
6. Loam
7. Sandy Clay Loam
8. Silty Clay Loam
9. Clay Loam
10. Sandy Clay
11. Silty Clay
12. Clay
13. Organic Materials
14. Water
15. Bedrock
16. Other
3. Sandy
loam
4.Silt
Loam
1. Sand
9.Clay
Loam
1. Sand
12 Clay
5 Silt
6. Loam
12. Clay
90 sec x 90 sec
Sand %
Clay %
Silt %
ICBC
Soil temperature and Soil Moisture
ERA40:
Global, Resolution: 2.5ox 2.5o, 4
layer
NCEP/NCAR reanalysis type I:
Global, Resolution: 2.5ox 2.5o
Outline
I.
Motivation
II. Common Land Model
III. Coupling CLM with RegCM3
IV. Preliminary results
ICTP RegCM3
with new packages
• Dynamics:
MM5 Hydrostatic (Grell et al 1994)
• Radiation:
CCM3.6.6 (Kiehl 1996)
• Large-Scale Clouds & Precipitation:
SUBEX (Pal et al 2000)
• Cumulus convection:
Grell (1993) + FC80 Closure
Anthes-Kuo (1977)
MIT/Emanuel (1991)
Betts-Miller (1993)
Zhang-McFarlane (new closure)
• Boundary Layer:
Holtslag (1990)
• Tracers/Aerosols:
Qian et al (2001); Solmon et al (2005)
includes dusts (Zakey, in progress)
• Land Surface:
BATS1e (Dickinson et al 1993)
SUB-BATS (Giorgi et al 2003)
CLM (Dai et al 2003, Dai & Bi, in
progress)
IBIS (Foley; Winter in progress)
• Ocean Fluxes:
BATS1e (Dickinson et al 1993)
Zeng et al (1998)
Air-Sea Coupling (MITogcm, OASIS
coupler, in progress)
• Nesting:
Numerous GCM/Reanalysis Interfaces
Double nesting (one-way)
• Computations:
User-Friendly
Multiple Platforms
Parallel Code (Pu & Bi, Gao)
The End
A Two-big-Leaf Model for Canopy Temperature,
Photosynthesis and Stomatal Conductance
(Journal of Climate, June 2004)
Two-stream Approximation for Canopy Albedoes
Calculation with the Solution for Singularity Point
(Journal of Climate, June 2004)
Singular points at two-stream
approximation radiative transfer model of
Sellers (1985)
1  0 or 2  0
1.
1
2.
and
2
are the coefficients of the projected area in solar
incident direction
s = m2 K 2 - (b 2 - c 2 ) = 0
b = [1- (1- b )w]
c = wb
 and 
are the the scattering coefficient of phytoelements and
upscatter parameters for diffuse and direct beams
New treatment for canopy interception with the
consideration of the fraction of convection and largescale precipitation
Turbulent transfer under canopy
(Treatment of under-canopy turbulence in land
models by Zeng et al. 2004, Journal of Climate)
Soil thermal and hydrological
processes with the consideration of
the depth to bedrock;
Depth to bedrock
Representation of the Land Surface and its
Overlying Near Surface Air Temperature in
Climate Models
• All covers smaller than 1% are either discarded,
and carried to the largest area tile in grid-squares,
with the exception of grass that is assumed to
always be at least 1%.
• Use of a grass tile temperature in place of an area
average or of an average of daily maximum and
minimum values rather than a 24-hr average can
change the estimates of daily temperatures over
regions by up to half a degree as a result of the
diurnal patterns of surface temperatures and their
dependence on cover.
Albedo Parameterization Based on MODIS and LDAS data
Nir MODIS
Nir CLM
Vis MODIS
Vis CLM
Nir MODIS
Nir CLM
Vis MODIS
Vis CLM
New Conceptual Model
Bare Soil Albedo
New Conceptual Model
Vegetation Albedo
New Conceptual Model
Static Localization Factor
New Conceptual Model
Optimization Solution
Parameters
Optimization Solver
FSQP FORTRAN Feasible Sequential Quadratic Programming
http://www.aemdesign.com/downloadfsqp.htm
Designed to find the optimal solution for the minimization of the
maximum of a set of smooth objective functions subject to
equality and inequality constraints, linear or nonlinear, and simple
bounds on the variables. It requires the accurate definition of the
objective functions and constraint functions as well as the
gradients of these functions to achieve a robust solution.
Zhou, J. L., A. L. Tits, and C. T. Lawrence, 1997: User’s Guide for FFSQP Version 3.7: A FORTRAN Code
for Solving Constrained Nonlinear (Minimax) Optimization Problems, Generating Iterates Satisfying
All Inequality and Linear Constraints. Institute for Systems Research, University of Maryland,
Technical Report SRC-TR-92-107r5, College Park, MD 20742, 44 pp.
Grassland
Nir MODIS
Nir Model
Vis MODIS
Vis Model
OLD
NEW
NEW correlation
NEW relative bias
OLD correlation
OLD relative bias
Direct Visible
Direct Near Infrared
NEW correlation
NEW relative bias
OLD correlation
OLD relative bias
Diffuse Visible
Diffuse Near Infrared