Specifying land surface characteristics in general circulation models: Soil profile data set and derived water‐holding capacities
- 1 March 1993
- journal article
- research article
- Published by American Geophysical Union (AGU) in Global Biogeochemical Cycles
- Vol. 7 (1) , 97-108
- https://doi.org/10.1029/92gb01822
Abstract
A standardized global data set of soil horizon thicknesses and textures (particle size distributions) has been compiled from the Food and Agriculture Organization of the United Nations/United Nations Educational, Scientific, and Cultural Organization (FAO/UNESCO) Soil Map of the World, Vols. 2–10 [1971–1981]. This data set was developed for use by the improved land‐surface hydrology parameterization designed by Abramopoulos et al. [1988] for the Goddard Institute for Space Studies General Circulation Model II (GISS GCM). The data set specifies the top and bottom depths and the percent abundance of sand, silt, and clay of individual soil horizons in each of the 106 soil types cataloged for nine continental divisions. When combined with the World Soil Data File [Zobler, 1986], the result is a l°×l° global data set of variations in physical properties throughout the soil profile. These properties are important in the determination of water storage in individual soil horizons and exchange of water with the lower atmosphere within global climate models. We have used these data sets, in conjunction with the Matthews [1983] global vegetation data set and texture‐based estimates of available soil moisture, to calculate the global distributions of soil profile thickness, potential storage of water in the soil profile, potential storage of water in the root zone, and potential storage of water derived from soil texture. Comparisons with the water‐holding capacities used in the GISS Model II show that our derived values for potential storage of water are consistently larger than those previously used in the GISS GCM. Preliminary analyses suggest that incorporation of this data set into the GISS GCM has improved the model's performance by including more realistic variability in land surface properties.This publication has 22 references indexed in Scilit:
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