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An alternative approach to evaluating the correlation skill of near-term climate predictions is proposed. The approach separately quantifies the skill of the initialized and uninitialized components of the forecast and their contributions to overall correlation skill. The initialized component consists of the predictable part of the internally generated natural variability and that part of the externally forced component affected by initialization. The methodology is applied to results from the latest Canadian Centre for Climate Modelling and Analysis' decadal prediction system. The initialized component of annual mean temperature forecasts exhibits regional skill over ocean and land. The contribution to overall skill is modest at longer ranges and for multiyear temperature averages, partly because of the strong temperature response to external forcing. Larger ensembles increase the percentage of global area with predictable variance due to initialization and the contribution to overall skill from the initialized component of the forecast.

Plain Language Summary Climate evolution on decadal timescales depends on the interplay between an externally forced component (due to changes in greenhouse gases, aerosols, land use, etc.) and internally generated natural variability. The climate consequences of the externally forced component may be estimated from climate simulations with no expectation that the time-dependent behavior of the internally generated component will match observations. A decadal prediction, by contrast, attempts to forecast the actual time-dependent evolution of climate variables by initializing a forecast model with observation-based information and integrating forward in time. Initialization affects both the forced and internally generated components, and a novel approach is proposed to quantify the extent to which initialization affects their forecasting skill. This is of particular interest for temperature, which exhibits a strong forced component resulting from a warming climate. The approach indicates the "added value" of initialized climate predictions over climate simulations which may be of use for planning and decision making.