Drift correction is an important step before using the outputs of decadal prediction experiments and has seen considerable research. However, most drift correction studies consider a relatively small sample of variables and models. Here, we present a systematic application of the existing drift correction strategies for decadal predictions of various sea surface temperature‐based metrics from a suite of five state‐of‐the‐art climate models (CanCM4i1, GFDL‐CM2.1, HadCM3i2&i3, MIROC5, and MPI‐ESM‐LR). The best method of drift correction for each metric and model is reported. Preliminary analysis suggests that there is no single method of drift correction that consistently performs best. Initial condition‐based drift correction provides the lowest errors in most regions for MIROC5 and the two HadCM3 models, whereas the trend‐based drift correction produces lowest errors for CanCM4i1, GFDL‐CM2.1, and MPI‐ESM‐LR over the largest share of the area. There is no merit in using a k‐nearest neighbor approach for these drift correction methods. Further, in almost all cases, the multimodel ensemble outperforms the individual models, and thus, the study conclusively suggests using forecasts based on multimodel averages. We also show some additional benefit to be gained by drift correcting each model/metric using their best correction method prior to model averaging and suggest that the results presented here would help potential users expend time and resources judiciously while dealing with outputs from these experiments.

Sea surface temperature anomaly climate indices in the tropical Pacific and Indian Oceans are statistically significant predictors of seasonal rainfall in the Indo-Pacific region. On this basis, this study evaluates the predictability of nine such indices, at interannual timescales, from the decadal hindcast experiments of four general circulation models. A Monte Carlo scheme is applied to define the periods of enhanced predictability for the indices. The effect of a recommended drift correction technique and the models' capabilities in simulating two specific El Niño and La Niña events are also examined. The results indicate that the improvement from drift correction is noticeable primarily in the full-field initialized models. Models show skillful predictability at timescales up to maximum a year for most indices, with indices in the tropical Pacific and the Western Indian Ocean having longer predictability horizons than other indices. The multi model ensemble mean shows the highest predictability for the Indian Ocean West Pole Index at 25 months. Models simulate the observed peaks during the El Niño and La Niña events in the Niño 3.4 index with limited success beyond timescales of a year, as expected from the predictability horizons. However, our study of a small number of events and models shows full-field initialized models outperforming anomaly initialized ones in simulating these events at annual timescales.

Recent studies examining the fidelity of decadal hindcast experiments from phase 5 of the Coupled Model Intercomparison Project have highlighted the need for larger ensembles of forecasts, compared to the initial five yearly spaced initializations, to help correct for model biases (drift). This study quantifies differences in the two drift estimates in sea surface temperature (SST) and SST anomaly (SSTA) predictions, between experiments initialized every 5 years and those initialized every year. The effect of the recommended mean drift correction, on the two sets of predictions, is also analyzed. Our results indicate that differences between the SST drift estimates are largest over the tropical Pacific. Moreover, this difference is large for Niño 3.4 and almost negligible for the global average SSTA. Drift correction as per the mean drift from the 5 year case leads to spurious peaks in the drift‐corrected Niño 3.4 (and the tropical Pacific) and sporadic improvements in skill. This problem with Niño 3.4 stems from an aliasing that occurs during the drift calculation that results from a combination of the timing of major El Niño events in relation to the initialization dates. The study recommends accounting for such sampling effects while considering any subset of the full data set.