A dataset for intercomparing the transient behavior of dynamical model-based subseasonal to decadal climate predictions

Climate predictions using coupled models in different time scales, from intraseasonal to decadal, are usually affected by initial shocks, drifts and biases which reduce the prediction skill. These arise from inconsistencies between different components of the coupled models and from the tendency of the model state to evolve from the prescribed initial conditions toward its own climatology over the course of the prediction. Aiming to provide tools and further insight into the mechanisms responsible for initial shocks, drifts and biases, this paper presents a novel dataset developed within the Long Range Forecast Transient Intercomparison Project, LRFTIP. This dataset has been constructed by averaging hindcasts over available prediction years and ensemble members to form a hindcast climatology that is a function of spatial variables and lead time, and thus results in a useful tool for characterizing and assessing the evolution of errors as well as the physical mechanisms responsible for them. A discussion on such errors at the different time scales is provided along with plausible ways forward in the field of climate predictions.
The authors would like to thank the two anonymous reviewers, whose suggestions helped improve and clarify several aspects of the manuscript, as well as Editor Dr. Stephen Griffies. In addition, Hai Lin and Yuhei Takaya provided helpful comments about the behavior of the ECCC-S2S and JMA-S2S models, respectively. The assistance of Marina Trubina in constructing S2S hindcast climatologies for the LRFTIP dataset is also kindly acknowledged.
Peer Reviewed
Postprint (author's final draft)