Climate models initialized near the observed state tend to drift toward their own climatology. This drift typically is removed during post-processing by subtracting a lead-time and start-month dependent climatology estimated from a recent 30-year period. Unfortunately, this method cannot correct long-term trend errors. This paper proposes an alternative approach that corrects both mean and trend errors as well as several other types of errors. The core idea is to fit observations and forecasts to separate forced autoregressive models, called ARX models, and then use the ARX models to predict the forecast error, which may then be removed. This approach is illustrated with climate forecasts from the SPEAR model, a contributor to the North American Multi-Model Ensemble (NMME). The proposed method is shown to outperform traditional corrections. The superior performance is due to the fact that SPEAR has non-stationary errors in the form of trend and initialization errors that cannot be corrected by the traditional method. Comparison of the SPEAR and observation ARX models provides a novel process-oriented diagnostic and indicates that SPEAR’s trend errors are due to an exaggerated response to radiative forcing. Because SPEAR is used to generate initial conditions via an ensemble data assimilation system, its trend errors propagate through the data assimilation system to create spurious trends in the initial conditions. Indeed, a significant trend error exists in the first month, and these errors can be replicated with a one-dimensional data assimilation system in which the first guess comes from an ARX model that emulates SPEAR.
