The characteristics of six global soil wetness products, four produced by land surface model calculations and two from microwave remote sensing estimates, have been examined. The goal of this study is to determine whether there exists an optimal data set for the initialization of the land surface component of global weather and climate forecast models. We validate their abilities to simulate the phasing of the annual cycle and to accurately represent interannual variability in soil wetness by comparing to available in situ measurements. Because soil wetness climatologies vary greatly among land surface models, and models have different operating ranges for soil wetness (i.e., very different mean values, variances, and hydrologically critical thresholds such as the point where evaporation occurs at the potential rate or where surface runoff begins), one cannot simply take the soil wetness field from one product and apply it to an arbitrary LSS as an initial condition without experiencing some sort of initialization shock. We propose a means of renormalizing soil wetness based on the local statistical properties of this field in the source and target models, to allow a large number of climate models to apply the same initialization in multi-model studies or inter-comparisons. As a test of feasibility, we apply renormalization among the model-derived products to see how it alters the character of the soil wetness climatologies.