Sea surface temperature (SST) and salinity (SSS) time series from four ocean weather stations and data from an integration of the GFDL coupled ocean-atmosphere model are analyzed to test the applicability of local linear stochastic theory to the mixed-layer ocean. According to this theory, mixed-layer variability away from coasts and fronts can be explained as a ‘red noise’ response to the ‘white noise’ forcing by atmospheric disturbances. At one weather station, Papa (northeast Pacific), this stochastic theory can be applied to both salinity and temperature, explaining the relative redness of the SSS spectrum. Similar results hold for a model grid point adjacent to Papa, where the relationships between atmospheric energy and water fluxes and actual changes in SST and SSS are what is expected from local linear stochastic theory. At the other weather stations, this theory cannot adequately explain mixed-layer variability. Two oceanic processes must be taken into account: at Panulirus (near Bermuda), mososcale eddies enhance the observed variability at high frequencies. At Mike and India (North Atlantic), variations in SST and SSS advection, indicated by the coherence and equal persistence of SST and SSS anomalies, contribute to much of the low frequency variability in the model and observations. To achieve a global perspective, TOPEX altimeter data and model results are used to identify regions of the ocean where these mechanisms of variability are important. Where mesoscale eddies are as energetic as at Panulirus, indicated by the TOPEX global distribution of sea level variability, one would expect enhanced variability on short time scales. In regions exhibiting signatures of variability similar to Mike and India, variations in SST and SSS advection should dominate at low frequencies. According to the model, this mode of variability is found in the circumpolar ocean and the northern North Atlantic, where it is associated with the irregular oscillations of the model’s thermohaline circulation.