2016) both aim to remove effects of the initial model drift, but are only partly successful and do not directly confront conditional model error. Anomaly initialization (e.g., Hazeleger et al. 2004), although the real-world problem may be considerably more complex ( Smith 2001). This “shadowing” approach has been applied to relatively simple models (e.g., Judd et al. When model-state space trajectories do not coincide with trajectories in nature, we could try to find and initialize those model trajectories that are closest (in some sense) to nature. At longer forecast leads, this problem may be worsened by the difficulty of spinning up the deeper oceanic component of the initialization. Initialization shock, which can arise when forecast error rapidly develops from an initial imbalance between the analyzed initial state and all possible model states, degrades forecast skill in the presence of nonlinear air–sea interactions ( Mulholland et al. CGCM seasonal forecast skill is significantly impacted by these model errors (e.g., Barnston et al. Models continue to suffer from both unconditional biases in their mean states, such as the double ITCZ and cold tongue bias (e.g., Li and Xie 2014), and conditional biases in their variability, such as a westward shift of ENSO (e.g., Joseph and Nigam 2006). While coupled models have improved, they remain imperfect. Both model and initialization improvements have driven advances in forecast skill, with considerable effort devoted to developing data assimilation schemes ( Ji and Leetmaa 1997 Stockdale et al. 2013), much of which is related to predictions of tropical sea surface temperature (SST) and especially of El Niño–Southern Oscillation (ENSO) ( Jin et al. Operational prediction centers worldwide use coupled atmosphere–ocean general circulation models (CGCMs) to conduct routine initialized seasonal forecasts with actionable skill of 6–12 months in advance ( Doblas-Reyes et al. Seasonal forecast skill has significantly improved over the past three decades ( Barnston et al. The model-analog method could provide a baseline for forecast skill when developing future models and forecast systems. This study suggests that with little additional effort, sufficiently realistic and long CGCM simulations provide the basis for skillful seasonal forecasts of tropical Indo-Pacific SST anomalies, even without sophisticated data assimilation or additional ensemble forecast integrations. Despite initializing with a relatively large ensemble spread, model-analogs also reproduce each CGCM’s perfect-model skill, consistent with a coarse-grained view of tropical Indo-Pacific predictability. In the eastern equatorial Pacific, model-analog hindcast skill exceeds that of the NMME. Deterministic and probabilistic skill measures of these model-analog hindcast ensembles are comparable to those of the initialized NMME hindcast ensembles, for both the individual models and the multimodel ensemble. Hindcasts are then made for leads of 1–12 months during 1982–2015. This technique is applied to four of the eight CGCMs comprising the North American Multimodel Ensemble (NMME) by selecting from prior long control runs those model states whose monthly tropical Indo-Pacific SST and SSH anomalies best resemble the observations at initialization time. The subsequent evolution of those “model-analogs” yields a forecast ensemble, without additional model integration. Here we explore initializing directly on a model’s own attractor, using an analog approach in which model states close to the observed initial state are drawn from a “library” obtained from prior uninitialized CGCM simulations. Seasonal forecasts made by coupled atmosphere–ocean general circulation models (CGCMs) undergo strong climate drift and initialization shock, driving the model state away from its long-term attractor.
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