The second version of California Reanalysis Downscaling at 10km (CaRD10v2)

- a part of REBI model intercomparison project.

Most of the physics are same as CaRD10

The physical processes included in the model are listed in the table below. The physical parameterization schemes used in RSM are fully tested in its global model counterpart - the Global Spectral Model - with ensemble AMIP type runs. The skill of the simulation is
reasonable and comparable to many other global models (Robertson et al., 2004). For the application of the schemes to high resolution regional downscaling by RSM, no explicit changes of the physical processes are applied, except the horizontal diffusion.

Robertson, A.W., U. Lall, S.E. Zebiak, and L. Goddard, 2004: Improved Combination of Multiple Atmospheric GCM Ensembles for Seasonal Prediction. Mon. Wea. Rev., 132, 2732–2744.

Land surface scheme

[ only for CaRD10 ] Among these physical processes, particular mention will be given to the Oregon State University Land Scheme (Pan and Mahrt, 1987; Mahrt and Ek, 1984; Mahrt and Pan, 1984; Ek and Mahrt, 1991) and the radiation. The land scheme consists of two soil layers, 10 cm and 190 cm thick, where soil moisture and soil temperature are predicted. Evaporation from the land surface is divided into two parts; direct evaporation and transpiration. The formula of Chen and Dudhia (2001a, b) is used for direct evaporation (Kanamitsu and Mo, 2003). The snow model is a simple 1-layer energy balance model. Other details of the scheme are described in Chen et al. (1996). The vegetation type, vegetation fraction, and soil type are fixed climatology and do not evolve during the 57 years of downscaling.

OSU includes 12 United States Geological Survey (USGS) vegetation types. One vegetation type in one grid cell. No dynamic vegetation.

There are 16 soil types from the State Soil Geographic Database (STATSGO; Miller and White 1998). Soil properties were specified by the analysis of Cosby et al. (1984).

Pan, H.-L. and L. Mahrt, 1987: Interaction between soil hydrology and boundary layer developments. Boundary-Layer Meteor., 38, 185-202.
Mahrt, L., and M. Ek, 1984: The Influence of Atmospheric Stability on Potential Evaporation. J. Appl. Meteor., 23, 222–234.
Mahrt, L., and H.L. Pan, 1984: A two layer model for soil hydrology. Boundary-Layer Meteor. 29, 1-20.
Ek M., and L. Mahrt, 1991: A Formulation for Boundary-Layer Cloud Cover. Annales Geophysicae, 9, 716-724.
Chen, F., and J. Dudhia, 2001: Coupling an Advanced Land Surface–Hydrology Model with the Penn State–NCAR MM5 Modeling System. Part I: Model Implementation and Sensitivity. Mon. Wea. Rev., 129, 569–585.
Chen, F., and J. Dudhia, 2001: Coupling an Advanced Land Surface–Hydrology Model with the Penn State–NCAR MM5 Modeling System. Part II: Preliminary Model Validation. Mon. Wea. Rev., 129, 587–604.
Kanamitsu, M., and K.C. Mo, 2003: Dynamical Effect of Land Surface Processes on Summer Precipitation over the Southwestern United States. J. Climate, 16, 496–509.
Chen, F., and Coauthors, 1996: Modeling of land surface evaporation by four schemes and comparison with FIFE observations. J. Geophys. Res., 101, 7251-7268.
Miller, D. A., and R. A. White, 1998: A conterminous United States multilayer soil characteristics dataset for regional climate and hydrology modeling. Earth Interactions, 2, 1-26. [Available online at EarthInteractions.org.].
Cosby, B. J., G. M. Hornberger, R. B. Clapp, and T. R. Ginn, 1984: A statistical exploration of the relationships of soil moisture characteristics to the physical properties of soils. Water Resour. Res., 20, 682-690.

[ only for CaRD10v2 ] CaRD10v2 uses four-layer Noah land surface scheme (Ek et al, 2003; DeHaan et al., 2007)) instead of OSU.

The four layers are 10, 30, 60, and 100cm thick and the root zone depth is spatially varying (dependent on vegetation classes) rather than fixed (2 meters for all vegetation classes) as in the OSU. The volumetric soil ice content at each soil layer is added as a new prognostic variable. The ice content is predicted as a function of soil temperature, soil moisture content, and soil type. The ice content in the soil water significantly influences the infiltration rate. Total and liquid soil moisture are prognostic state variables and the difference between the two represents frozen soil moisture. The frozen soil physics (Koren et al, 1999) includes the impact of soil freezing/thawing on soil heat sources/sinks, vertical movement of soil moisture, soil thermal conductivity and heat capacity, and surface infiltration of precipitation. Snow pack physics are also improved with the snow density predicted as a function of time and snow temperature. The snow thermal conductivity is affected by the change in snow density and thus the snowmelt process is more accurately simulated. The snow albedo is also predicted considering the partial snow cover in the grid box, which is a function of snow depth. The deep snow albedo is constrained by the geographically varying annual maximum snow albedo dataset as a function of vegetation type. In summary, the prognostic variables of the Noah scheme are soil temperature, moisture ans soil ice content at four soil layers, canopy water content, snow depth, snow density, and snow albedo. [this paragraph from DeHaan et al. 2007]

DeHaan, L., M. Kanamitsu, C-H Lu, and J. Roads, 2007: A comparison of the Noah and OSU Land Surface Models in the ECPC Seasonal Forecast model. J. Hydromet. (in press). See References page for a PDF copy.
Ek M. B., K. E. Mitchell, Y. Lin, E. Rogers, P. Grunmann, V. Koren, G. Gayno, J. D. Tarpley, 2003: Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model, J. Geophys. Res., 108 (D22), 8851, doi:10.1029/2002JD003296.
Koren, V., J. Schaake, K. Mitchell, Q.-Y. Duan, F. Chen, J. M. Baker, 1999: A parameterization of snowpack and frozen ground intended for NCEP weather and climate models, J. Geophys. Res., 104(D16), 19569-19586, 10.1029/1999JD900232.

Radiation

Both short and long wave radiation schemes are taken from M.-D. Chou (Chou and Suarez 1994; Chou and Lee 1996). Cloudiness is computed from relative humidity and vertical motion, as well as from marine boundary layer depth and intensity (Slingo, 1987). These clouds interact with the radiation scheme.

Chou, M.D. and M. J. Suarez, 1994: An efficient thermal infrared radiation parameterization for use in General Circulation Models. Technical Report Series on Global Modeling and Data Assimilation, National Aeronautical and Space Administration/TM-1994-104606, 3, 85 pp.
Chou, M.D., and K.T. Lee, 1996: Parameterizations for the Absorption of Solar Radiation by Water Vapor and Ozone. J. Atmos. Sci., 53, 1203–1208.
Slingo, J.M., 1987: The development and verification of a cloud prediction model for the ECMWF model. Quart. J. Roy. Meteor. Soc., 113, 899-927.

SSBC

Area average temperature and moisture in the regional domain are nudged to those of the reanalysis by the scale selective bias correction
(SSBC) scheme (Kanamaru and Kanamitsu, 2007). Therefore the effects of CO2 and aerosol on the downscaled analysis of large scale free atmosphere will be minimal. However, the surface fluxes will certainly be affected by CO2 and aerosol. These atmospheric compositions impact land states such as soil moisture and snow through the change in radiation flux reaching the ground. In CaRD10, the CO2 concentration is fixed at 348 ppm throughout the 57 years of integration. The aerosol is also fixed at seasonal climatological value by Koepke et al. (1997).

Koepke, P., M. Hess, I. Schult, and E.P. Shettle, 1997: Global aerosol data set. MPI Meteorologie Hamburg Report No. 243, 44 pp.

	CaRD10	CaRD10v2
Convection	Relaxed Arakawa-Schubert (Moorthi and Suarez 1992)	Same as CaRD10
Large scale condensation	Evaporation of rain included	Same as CaRD10
Shallow convection	Tiedtke scheme (Tiedtke 1983)	Same as CaRD10
Boundary layer	Non-local scheme (Hong and Pan 1996)	Same as CaRD10
Surface layer	Monin-Obukhov	Same as CaRD10
Long wave radiation	M.-D. Chou (Chou and Suarez 1994)	Same as CaRD10
Short wave radiation	M.-D. Chou (Chou and Lee 1996)	Same as CaRD10
Cloudiness	Slingo (Slingo 1987)	Same as CaRD10
Gravity wave drag	Pierrehumbert (Alpert et al. 1988)	Same as CaRD10
Vertical diffusion	Richardson number dependent	Same as CaRD10
Land model	OSU (Pan and Mahrt 1987) two soil layers (10cm and 190cm)	Noah four soil layers
Vegetation	12 types from USGS	different (details will be posted)
Direct evaporation	NCAR (Chen 1996)	Same?
Topography	Smoothed mean from USGS GTOPO30	Same as CaRD10
Domain size		Larger than CaRD10
Horizontal resolution	10 km	Same as CaRD10
Boundary sponge zone		Smaller than CaRD10
Horizontal diffusion
Soil	16 types from STATSGO
Cloud water prediction	N/A	Tiedtke (1993) and Iacobellis and Sommerville (2000)