1. 3.0 Method The study used the stand alone mode of the Joint UK Land Environment Simulator (JULES) version 3. JULES represents the land surface interaction in the Met Office Global Circulation Model known as the Unified Model. In JULES the earth surface is partitioned into grid boxes each consisting of nine tiles. There are five vegetative tiles ( Broad leaf, needle leaf, C4, C3, and shrubs) and four land tiles (urban, bare surface, lakes, and ice). Tiles are simulated separately and the grid box level output is the weighted average of all tiles. Two separate model runs were undertaken using a ½ degree data sets for a period of 21 years and a spin up of five years in the initial year. The first model run was based on a new soil map of the study region while the second model used the JULES standard soil data. Figure 2: : Land atmosphere energy interaction represented in a JULES Model. 3.1 Data Types I.Atmospheric forcing data: CRU-NCEP ½ degree 6 hourly climate data 1980-2006. II.Soil data: soil survey map of Nigeria 1996, MODIS generated soil visible albedo; the institute of Photogrammetry University of Technology Vienna(IPF TUW) satellite generated (ERS-1 and ERS-2) ½ degree surface soil moisture data 1991-2000 III.Vegetation: Normalized Difference Vegetation Index (NDVI ) 4.1 Soil parameters A soil survey map of Nigeria (1996) was digitized using Arc GIS and transformed into an ascii format. The most dominant soil type in each part of the study area was then used to develop the soil textural classes in Figure 3 based on the FAO 1973 textural classification. Subsequently, soil hydraulic parameters of b, θ s, k s and ψ s used in the Brooks and Corey (1964) model were calculated from these classes using the relationships of Cosby et al. (1984). 4.0 Results 4.2 Differences in modeled surface energy fluxes There is some variation in sensible and latent heat flux between the two simulations. Testing the level of variance between the two output(MSW) and within each group(MSB) represented by F=MSB/MSW in a one way analysis of variance the F (F-ratio) values calculated for the differences in grid box surface energy flux between the two modeled results has no significant value at α=0.05. The value was consistent at various periods of the modeled output as presented in figures 4 and 5. Figure 3: 17 soil textural classes in the study area Figure 4: F ratio of Sensible heat fluxFigure 5: F ratio of latent heat flux Figure 6: Differences in grid box averaged soil moisture as a fraction of saturation between the two models Figure 7: Comparing soil moisture modeled output (standard soil parameters red, and new soil parameters black lines) and satellite observations (green lines) Figure 1: The study area in North-eastern Nigeria 2.0 Rationale Soil moisture conditions are crucial in the forecast of weather and studying the anomalies in climate such as the causes of drought, floods and surface temperature variations (Robock et al, 2000). These anomalies are more pronounced in semi-arid regions. Increasing the heterogeneity of the land surface at the regional scale may likely reduce uncertainties in land surface models and improve simulation results. 4.3 Comparing soil moisture Averaged grid box moisture content as a fraction of saturation at the upper soil layer (10 cm deep) for the whole period under study was compared between the two model simulations (Fig. 7). This indicates a much lower values for the model with the new soil data set. The same results were compared with surface soil moisture (5 cm deep) data from satellite observation (Fig. 8). Although there is a difference in depth, there seems to be an agreement between the model results and observation only in the southern more wetter part of the study area. The differences widens during the dry season and northwards towards the much drier Sahel. 5.0 Summary There are differences in surface energy fluxes between the two simulations but when the results were subjected to statistical analysis appears to be insignificant. The simulation using the new soil data set produces a lower estimate of average grid box moisture content as a fraction of saturation. Results from both simulations differ from satellite observations during the period of water stress and in drier parts. Simulations produce low estimates of soil moisture in the drier north and only compares well with satellite observations in the southern part during the wet season. The reasons for the significant differences between simulations and observations will be explored by evaluating the soil model in JULES more comprehensively and reconfirming the soil parameters used. 6.0 References Brooks, R.H., Corey, A.T., 1964. Hydraulic properties of porous media. Hydrology Paper No.3, Civil Engineering Department, Colorado State University, Fort Collins, CO. Gaertner M.A., Dominguez M., and Garvert M. 2010 A Modelling Case-study of Soil Moisture-atmosphere coupling Q.J.R. Meteorological Society 136:485-495 Robock A., Vinikov K.Y., Srinivasan G., Entin J., Hollinger S. E., Speranskaya N.A., Liu S., and Namkhai A. 2000 The global soil moisture data bank. American Meteorological society 8:61,1281-1299. Cosby, B.J., G.M. Hornberger, B. Clapp, and T. R. Ginn, 1984. A Statistical Exploration of the Relationships of Soil Moisture Characteristics to the Physical Properties of Soils, Water Resources Research, 20(6), 682-690, 7.0 Acknowledgements We gratefully acknowledge for data access and support: PTDF Nigeria, Nicolas Viovy, LSCE, France, for the CRU NCEP data, Sietse Los, Swansea University, UK, for the NDVI data, Richard Kidd of IPF TUW for the satellite based surface soil moisture and Will Grey, MetOffice, UK for the MODIS albedo data. 1.0Introduction F In order to study the impact of increased variability in the representation of the land surface in land surface models (LSMs) at the regional level we introduced a new 0.5 grid soil data set into the Joint UK Land Environment simulator JULES (version 3). Model results of surface soil moisture, latent and sensible heat flux were compared with results of a model run using a standard soil data set. For model evaluation we used the satellite derived surface soil moisture data. The selected area of study in this case is the North-eastern part of Nigeria (Latitude 6°-14°N/Longitude 8°-15°E) an area markedly influenced by the West African Monsoon and the existence of a strong feedback mechanism between atmospheric conditions and the land surface (Gaertner et al., 2010). Exploring the impact of scale and enhanced soil parameterization on surface energy fluxes in land surface models (LSM) in a semi-arid region Department of Geography Bibi U.M., Kaduk J., Balzter H. Contact: umb2@le.ac.uk