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Information in decadal climate prediction arises from a well-initialized ocean state and from the predicted response to an external forcing. The length of time over which the initial conditions benefit the decadal forecast depends on the start date of the forecast. We characterize this state-dependent predictability for decadal forecasts of upper ocean heat content in the Community Earth System Model. We find regionally dependent initial condition predictability, with extended predictability generally observed in the extratropics. We also detect state-dependent predictability, with the year of loss of information from the initialization varying between start dates. The decadal forecasts in the North Atlantic show substantial information from the initial conditions beyond the 10-yr forecast window, and a high degree of state-dependent predictability. We find some evidence for state-dependent predictability in the ensemble spread in this region, similar to that seen in weather and subseasonal-to-seasonal forecasts. For some start dates, an increase of information with lead time is observed, for which the initialized forecasts predict a growing phase of the Atlantic multidecadal oscillation. Finally we consider the information in the forecast from the initial conditions relative to the forced response, and quantify the crossover time scale after which the forcing provides more information. We demonstrate that the climate change signal projects onto different patterns than the signal from the initial conditions. This means that even after the crossover time scale has been reached in a basin-averaged sense, the benefits of initialization can be felt locally on longer time scales. [ABSTRACT FROM AUTHOR]

El Niño-Southern Oscillation (ENSO) is the dominant mode of interannual variability in the tropical Pacific. However, the models in the ensemble from phase 5 of the Coupled Model Intercomparison Project (CMIP5) have large deficiencies in ENSO amplitude, spatial structure, and temporal variability. The use of stochastic parameterizations as a technique to address these pervasive errors is considered. The multiplicative stochastically perturbed parameterization tendencies (SPPT) scheme is included in coupled integrations of the National Center for Atmospheric Research (NCAR) Community Atmosphere Model, version 4 (CAM4). The SPPT scheme results in a significant improvement to the representation of ENSO in CAM4, improving the power spectrum and reducing the magnitude of ENSO toward that observed. To understand the observed impact, additive and multiplicative noise in a simple delayed oscillator (DO) model of ENSO is considered. Additive noise results in an increase in ENSO amplitude, but multiplicative noise can reduce the magnitude of ENSO, as was observed for SPPT in CAM4. In light of these results, two complementary mechanisms are proposed by which the improvement occurs in CAM. Comparison of the coupled runs with a set of atmosphere-only runs indicates that SPPT first improve the variability in the zonal winds through perturbing the convective heating tendencies, which improves the variability of ENSO. In addition, SPPT improve the distribution of westerly wind bursts (WWBs), important for initiation of El Niño events, by increasing the stochastic component of WWB and reducing the overly strong dependency on SST compared to the control integration. [ABSTRACT FROM AUTHOR]