Diagnosing Secular Variations in Retrospective ENSO Seasonal Forecast Skill Using CMIP5 Model‐Analogs.

Retrospective tropical Indo‐Pacific forecasts for 1961–2015 are made using 28 models from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) plus four models from the North American Multi‐Model Ensemble (NMME), using a model‐analog technique. Forecast ensembles are extracted from preexisting model simulations, by finding those states that initially best match an observed anomaly and tracking their subsequent evolution, requiring no additional model integrations. Model‐analog forecasts from the 10 "best" CMIP5 models have skill for sea surface temperature and precipitation comparable to that of both the NMME model‐analog forecast ensemble and (since 1982) traditional assimilation‐initialized NMME hindcasts. The El Niño–Southern Oscillation (ENSO) forecast skill has no trend over the 55‐year period, and its decadal variations appear largely random, although the skill does improve during epochs of increased ENSO activity. Including the CMIP5‐projected effects of external radiative forcings improves the tropical sea surface temperature skill of the model‐analog forecasts but not within the ENSO region. Plain Language Summary: Seasonal forecasts are made by starting a climate model from an initial estimate of the latest global three‐dimensional ocean, atmosphere, and land conditions and then using supercomputers to run the model's equations forward in time. These extensive calculations are only feasible at a few national operational centers and large research institutions. However, many similar models are also used for long simulations of the Earth's preindustrial climate, which are made freely available for climate change studies. We investigated whether seasonal forecasts might be drawn from the information already existing within these simulations, instead of by making new model computations. Within each simulation, we determine the best matches, or "model‐analogs," to current observed tropical Indo‐Pacific ocean surface conditions. How these analogs evolve over the next several months within each long simulation is then its seasonal forecast. We find that this much less expensive model‐analog technique is as skillful as the more traditional forecasting method. We then employed it to make forecasts during 1961–2015, using 28 existing climate simulations, significantly expanding previous forecast efforts. This study suggests that with little additional effort, sufficiently realistic and long existing climate model simulations can provide the basis for skillful seasonal forecasts. That is, anyone can be a climate forecaster. Key Points: Seasonal tropical Indo‐Pacific hindcasts for 1961‐2015 are made from 28 different CMIP5 models, with some as skillful as operational CGCMsENSO hindcast skill has no trend, but it is higher in strong‐ENSO epochs (1980s and 1990s) and lower in weak‐ENSO epochs (1970s and 2000s)Including the effects of projected external radiative forcings improves SST hindcast skill but not within the ENSO region [ABSTRACT FROM AUTHOR]