Assumptions made by global climate models (GCMs) regarding vertical overlap of fractional amounts of clouds have significant impacts on simulated radiation budgets. A global survey of fractional cloud overlap properties was performed using 2 months of cloud mask data derived from CloudSat-CALIPSO satellite measurements. Cloud overlap was diagnosed as a combination of maximum and random overlap and characterized by vertically constant decorrelation length Lcf* . Typically, clouds overlap between maximum and random with smallest Lcf * (medians ! 0 km) associated with small total cloud amounts C b , while the largest Lcf

* (medians ~3 km) tend to occur at C b near 0.7. Global median Lcf * is ~2 km with a slight tendency for largest values in the tropics and polar regions during winter. By crudely excising near-surface precipitation from cloud mask data, Lcf* were reduced by typically <1 km. Median values of Lcf * when Sun is down exceed those when Sun is up by almost 1 km when cloud masks are based on radar and lidar data; use of radar only shows minimal diurnal variation but significantly larger

*. This suggests that sunup inferences of Lcf Lcf * might be biased low by solar noise in lidar data. Cloud mask cross-section lengths L of 50, 100, 200, 500, and 1000 km were considered. Distributions of Lcf * are mildly sensitive to L thus suggesting the convenient possibility that a GCM parametrization of Lcf * might be resolution-independent over a wide range of resolutions. Simple parametrization of Lcf * might be possible if excessive random noise in C b , and hence radiative fluxes, can be tolerated. Using just cloud mask data and assuming a global mean shortwave cloud radiative effect of -45 W m-2, top of atmosphere shortwave radiative sensitivity to Lcf * was estimated at 2 to 3 W m-2 km-1.