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									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)          BATS1e (Dickinson et al 1993)
    Non-hydrostatic (MM5 or WRF, in             SUB-BATS (Giorgi et al 2003)
      progress)                                 CLM (Dai et al 2003, Dai & Bi, in
• Radiation:                                       progress)
    CCM3.6.6 (Kiehl 1996)                       IBIS (Foley; Winter in progress)
• Large-Scale Clouds & Precipitation:        • Ocean Fluxes:
    SUBEX (Pal et al 2000)                      BATS1e (Dickinson et al 1993)
• Cumulus convection:                           Zeng et al (1998)
                                                Air-Sea Coupling (MITogcm, OASIS
    Grell (1993) + FC80 Closure                    coupler, in progress)
    Anthes-Kuo (1977)
                                             • Nesting:
    MIT/Emanuel (1991)
                                                Numerous GCM/Reanalysis Interfaces
    Betts-Miller (1993)
                                                Double nesting (one-way)
• Boundary Layer:
                                             • Computations:
    Holtslag (1990)
                                                User-Friendly
• Tracers/Aerosols:                             Multiple Platforms
    Qian et al (2001); Solmon et al (2005)      Parallel Code (Pu & Bi, Gao)
    includes dusts (Zakey, in progress)
                   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                      RegCM: BATS
                                        RegCM: BATS-Zeng

    BATS vs. Zeng
• The BATS1e bulk aerodynamic
  algorithm uses the Monin-
  Obukhov similarity relations
  without special treatments of
  convective or very stable
  conditions.
   – Overestimate latent heat in both
                                          RegCM: Zeng
     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
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 :                                  Vertical :
• Every surface grid cell can be              • one vegetation layer.
  subdivided into any number of tiles.        • 10 soil layers, and the thickness: 17.5,
• Energy and water balance calculations         27.6, 45.5, 75.0, 123.6, 203.8, 336.0,
  are performed over each tile at every         553.9, 913.3, and 1137.0 mm with a
  time step, and each tile maintains its        total thickness of 3430 mm.
  own state variables.                        • up to 5 snow layers (depending on
• The tiles in a grid square respond to the     snow depth). Contrary to more usual
  mean conditions in the overlying              practice, the snow layers from top to
  atmospheric grid box, and this grid box,      bottom are numbered as negative
  in turn, responds to the area-weighted        values.
  fluxes of heat and moisture from the
  tiles.
• The tiles within a grid square do not
  interact with each other directly.
               – Mosaic treatment
    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,                    Community Land Model

    Houser at GSFC/NASA,
    Zeng at U. Arizona,
    Yang at UT Austin,
    Denning at CSU
New development in Common Land Model

1) Two big leaf model for leaf temperatures, photosynthesis-
   stomatal 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
                               ECMWF
               PreProc          ERA40             Main          PostProc
                                NNRP1
                                NNRP2                           NetCDF output
  Global                       EH5OM                            FERRET, NCL
                 Global 1x1
Terrrestrial                   FVGCM
                 SST Data
   Data                        HadAMH
                               REGCM             GrADS output
                                 ……            ATM.yyyymmddhh    POSTPROC
                                               RAD.yyyymmddhh
                    SST                        SRF.yyyymmddhh
                                               CHE.yyyymmddhh
 Terrain                         ICBC
                                                                POSTPROCv5d



                              ICBCyyyymmddhh
                                                                     Vis5D
                                    ……

                                                   RegCM
                                                    Main
      DOMAIN.INFO                                                 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
                                         15. Mixed Forest
4. Mixed Dry-land / Irrigated Cropland
    and Pasture                          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
                                         20. Herbaceous Tundra
8. Shrubland
                                         21. Wooded Tundra
9. Mixed Shrubland/Grassland
                                         22. Mixed Tundra
10. Savanna
                                         23. Bare Ground Tundra
                                         24. Snow or Ice
1. Urban    1. Urban     7.          90 sec x 90 sec
                         Grassland


16. Lake    7.           7.
            Grassland    Grassland


15. Mixed   19. Barren   16. Lake
forest




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
                                90 sec x 90 sec
3. Sandy   4.Silt    1. Sand
loam       Loam

9.Clay     1. Sand   12 Clay
Loam

5 Silt     6. Loam   12. Clay




                                              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:                                  • Land Surface:
    MM5 Hydrostatic (Grell et al 1994)          BATS1e (Dickinson et al 1993)
• Radiation:                                    SUB-BATS (Giorgi et al 2003)
    CCM3.6.6 (Kiehl 1996)                       CLM (Dai et al 2003, Dai & Bi, in
                                                   progress)
• Large-Scale Clouds & Precipitation:           IBIS (Foley; Winter in progress)
    SUBEX (Pal et al 2000)                   • Ocean Fluxes:
• Cumulus convection:                           BATS1e (Dickinson et al 1993)
    Grell (1993) + FC80 Closure                 Zeng et al (1998)
    Anthes-Kuo (1977)                           Air-Sea Coupling (MITogcm, OASIS
    MIT/Emanuel (1991)                             coupler, in progress)
    Betts-Miller (1993)                      • Nesting:
    Zhang-McFarlane (new closure)               Numerous GCM/Reanalysis Interfaces
• Boundary Layer:                               Double nesting (one-way)
    Holtslag (1990)                          • Computations:
• Tracers/Aerosols:                             User-Friendly
    Qian et al (2001); Solmon et al (2005)      Multiple Platforms
    includes dusts (Zakey, in progress)         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.        1  0 or 2  0
     1   and   2    are the coefficients of the projected area in solar
                      incident direction

2.        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 large-
scale 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




OLD




      Vis MODIS               Vis Model




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

								
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