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 cf*. Typically, clouds overlap between maximum and random with smallest cf* (medians → 0 km) associated with small total cloud amounts , while the largest cf* (medians ∼3 km) tend to occur at near 0.7. Global median cf* 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, cf* were reduced by typically <1 km. Median values of cf* 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 cf*. This suggests that sunup inferences of cf* 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 cf* are mildly sensitive to L thus suggesting the convenient possibility that a GCM parametrization of cf* might be resolution-independent over a wide range of resolutions. Simple parametrization of cf* might be possible if excessive random noise in , 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 cf* was estimated at 2 to 3 W m−2 km−1.