Global Soil Wetness Project: Forecast and assimilation experiments performed at Meteo-France

Global soil moisture data of high quality and resolution are not available by direct observation, but are useful as boundary and initial conditions in comprehensive climate models. In the framework of the Global Soil Wetness Project (GSWP), the ISBA land-surface scheme of Meteo-Fraace has been force d with meteorological observations and analyses in order to study the feasi bility of producing a global soil wetness climatology at a 1 degrees x 1 de grees horizontal resolution between January 1987 and December 1988. A contr ol experiment and several sensitivity tests have been performed, suggesting that soil moisture remains one of the most difficult climatological parame ters to model and that any computed climatology must be considered with gre at caution. The prescription of the soil depth is particularly critical, sh owing the relevance of the absolute value of the soil water content and the interest for land surface schemes to include a deep layer beyond the rooti ng depth. Compared to a river flow climatology, the runoff simulated over l arge river basins seems to be underestimated because of deficiencies in bot h the ISBA scheme and the GSWP experiment design. In order to obtain a more reliable climatology, a global reanalysis of soil moisture has been attemp ted, using a sequential optimal interpolation technique, in which soil mois ture is corrected by iterative comparison between simulated and observed ne ar-surface air temperature and relative humidity. Preliminary tests have be en performed for July 1987, showing the potential of this method in idealiz ed conditions. In practice, many uncertainties, either in the observations, the land surface properties or the atmospheric forcing, ate liable to jeop ardize the quality of the reanalysis, suggesting the need for more consiste nt data within the GSWP framework. Some outlooks are presented for improvin g the robustness of the assimilation technique, which lead to encouraging r esults.