The geophysical information required by MSC-E models include land cover, leaf area index and oceanological input data.

Land cover

Land cover data is mostly required for evaluation of the dry deposition velocities and assessment of ecosystem-specific depositions. Currently a preliminary land cover dataset developed by the Coordinating Centre for Effects (CCE) is used in the model. The dataset consider 18 landuse/landcover categories listed below.

1. Temperate coniferous forest 10. Semi-natural
2. Temperate deciduous forest 11. Mediterranean scrub
3. Mediterranean needleleaf forest 12. Wetlands
4. Mediterranean broadleaf forest 13. Tundra
5. Temperate crops 14. Desert/Barren
6. Mediterranean crops 15. Inland water
7. Root crops 16. Sea waster
8. Grasslands 17. Ice
9. Wheat 18. Urban

Example of spatial distribution of forests (sum all types) in the EMEP region, expressed as a per sent of grid-cell area is shown below.


Leaf Area Index (LAI)

Leaf Area Index data set is used for the description of POP gaseous exchange between the atmosphere and vegetation. The Leaf Area Index for a given cell implies the ratio between the area of leaves in the cell to its total area. The geographically resolved LAI data with monthly resolution was adopted from CD-ROM of NASA Goddard Space Flight Center [Sellers et al., 1994, 1995] and redistributed to the MSCE-POP model grid system.


Oceanological input data


Fig.1. Calculation cycle of the POP model

Multi-media simulations of pollutants dispersion in the environment require gridded oceanological input data (sea currents, water temperature, etc.) In order to prepare self-consistent datasets Parallel Ocean Program (POP) was chosen as the oceanic pre-processor for the GLEMOS model. This is a freely available model developed at Los Alamos National Laboratory. The POP model is the ocean component of the Community Climate System Model CESM - a fully-coupled, global climate model that provides state-of-the-art computer simulations of the Earth's past, present, and future climate states.

The calculation cycle of POP model is shown in Fig. 1. To initialize global-scale oceanic calculations monthly climatological data on potential temperature and water salinity are used (NOAA NODC World Ocean Atlas 2005). The ECMWF 6-hour meteorological re-analysis data and daily 3-D data on the ocean potential temperature and salinity from ECMWF ORA S3 re-analysis are used for at the second stage of the spin-up and at the preprocessing stage as input information.

Some results of oceanic preprocessing for 2009 are presented in the figures. The spatial distributions of currents velocity components in the upper ocean layer are shown in Fig 2. It can be seen that the major currents (Equatorial, Gulf Stream, Kuroshio, Antarctic Circumpolar etc.) were reproduced.


a b
Fig. 2. Spatial distributions of zonal (a) and meridional (b) current velocities (cm/s) in the upper ocean layer on 31 Dec 2009


Calculated ocean currents are compared with fixed depth measurement data of Tropical Atmosphere Ocean (TAO) project. Annual mean modeled (1º×1º) and measured values at 10m depth are in good agreement (Fig. 3). Daily computed data for most of the stations correlates with measurements (Fig. 4 - an example for equatorial station in the Indian Ocean).

Fig. 3. Scatter plot for annual mean calculated and measured ocean current velocities
Fig. 4. Calculated and measured daily averaged zonal ocean current velocities (cm/s) in the Indian Ocean (00 N, 80.50 E) at depth 10 m



Sellers P.J., S.O. Los, C.J. Tucker, C.O. Justice, D.A. Dazlich, G.J. Collatz, and D.A. Randall [1995]. A revised land surface parameterization (SiB2) for atmospheric GCMs. Part 2: The generation of global fields of terrestrial biophysical parameters from satellite data. Submitted to Journal of Climate.

Sellers P.J., S.O. Los, C.J. Tucker, C.O. Justice, D.A. Dazlich, G.J. Collatz, and D.A. Randall [1994] A global 1 by 1 degree NDVI data set for climate studies. Part 2: The generation of global fields of terrestrial biophysical parameters from the NDVI. International Journal of Remote Sensing, v.15, No.17, pp.3519-3545.