Preliminary results of the coupling of CLM with ICTP RegCM3

Dai Yongjiu

1 ,

Bi Xunqiang

2

, Filippo Goirgi

2 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 progress)

• Radiation:

SUB-BATS (Giorgi et al 2003) CLM (Dai et al 2003, Dai & Bi, in progress) CCM3.6.6 (Kiehl 1996)

• Large-Scale Clouds & Precipitation:

IBIS (Foley; Winter in progress)

• Ocean Fluxes:

SUBEX (Pal et al 2000) BATS1e (Dickinson et al 1993)

• Cumulus convection :

Grell (1993) + FC80 Closure Anthes-Kuo (1977) Zeng et al (1998) Air-Sea Coupling (MITogcm, OASIS coupler, in progress)

• Nesting:

MIT/Emanuel (1991) Numerous GCM/Reanalysis Interfaces Betts-Miller (1993)

• Boundary Layer:

Double nesting (one-way)

• Computations:

Holtslag (1990)

• Tracers/Aerosols:

User-Friendly 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 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 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: 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.

2.

3.

4.

5.

Enough unevenly spaced layers to adequately represent soil temperature and soil moisture, and a multi-layer parameterization of snow processes;

An explicit treatment of the mass of liquid water and ice water and their phase change within the snow and soil system

; A runoff parameterization following the TOPMODEL concept;

A canopy photosynthesis-conductance model that describes the simultaneous transfer of CO2 and water vapor into and out of vegetation

; 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, Dickinson at Georgia Tech,

Common Land Model

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, 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)

7)

8) Soil thermal and hydrological processes with the consideration of the depth to bedrock;

Surface runoff and sub-surface runoff;

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

Global Terrrestrial Data Terrain

RegCM3 Modeling System Flow Chart

PreProc Global 1x1 SST Data SST ECMWF ERA40 NNRP1 NNRP2 EH5OM FVGCM HadAMH REGCM …… Main GrADS output ATM.yyyymmddhh

RAD.yyyymmddhh

SRF.yyyymmddhh

CHE.yyyymmddhh

ICBC PostProc NetCDF output FERRET, NCL POSTPROC 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* 1. Urban and Built-Up Land 2. Dry-land Cropland and Pasture 3. Irrigated Cropland and Pasture 4. Mixed Dry-land / Irrigated Cropland and Pasture 5. Cropland / Grassland Mosaic 6. Cropland/Woodland Mosaic 7. Grassland 8. Shrubland 9. Mixed Shrubland/Grassland 10. Savanna 11. Deciduous Broadleaf Forest 12. Deciduous Needleleaf Forest 13. Evergreen Broadleaf Forest 14. Evergreen Needleleaf Forest 15. Mixed Forest 16. Inland Water Bodies* 17. Herbaceous Wetland 18. Wooded Wetland 19. Barren or Sparsely Vegetated 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 5 Silt 1. Sand 9.Clay Loam 1. Sand 12 Clay 6. Loam 12. Clay

90 sec x 90 sec Sand % Clay % Silt %

ICBC

Soil temperature and Soil Moisture

ERA40

: Global, Resolution: 2.5

o x 2.5

o , 4 layer NCEP/NCAR reanalysis type I: Global, Resolution: 2.5

o x 2.5

o

Outline

I.

Motivation II.

Common Land Model III. Coupling CLM with RegCM3

IV. Preliminary results

ICTP RegCM3

• 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 with new packages

• 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) Anthes-Kuo (1977) MIT/Emanuel (1991) Betts-Miller (1993) Zeng et al (1998) Air-Sea Coupling (MITogcm, OASIS coupler, in progress)

• Nesting:

Zhang-McFarlane (new closure)

• Boundary Layer:

Holtslag (1990)

• Tracers/Aerosols:

Numerous GCM/Reanalysis Interfaces Double nesting (one-way)

• Computations:

User-Friendly Qian et al (2001) ; Solmon et al (2005) includes dusts (Zakey, in progress) 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.

 1  0

or

 1 and  2  2  0 are the coefficients of the projected area in solar incident direction 2.

s

=

m

2

K

2 (

b

2 -

c

2 ) = 0

b

= [ 1 -

c

=  and 

wb

( 1 are the the scattering coefficient of phytoelements upscatter parameters for diffuse and direct beams and

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 Vis MODIS Nir CLM Vis CLM

Nir MODIS Vis MODIS Nir CLM 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.

Nir MODIS Grassland Nir Model OLD NEW Vis MODIS Vis Model

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