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From January through March 2017, a series of extreme precipitation events occurred in coastal Peru, causing severe floods with hundreds of human casualties and billions of dollars in economic losses. The extreme precipitation was a result of unusually strong recurrent patterns of atmospheric and oceanic conditions, including extremely warm coastal sea surface temperatures (SST) and weakened trade winds. These climatic features and their causal relationship with the Peruvian precipitation were examined. Diagnostic analysis and model experiments suggest that an atmospheric forcing in early 2017, which was moderately linked to the Trans-Niño Index (TNI), initiated the local SST warming along coastal Peru that later expanded to the equator. In January 2017, soil moisture was increased by an unusual expansion of Amazonian rainfall. By March, localized and robust SST warming provided positive feedback to the weakening of the trade winds, leading to increased onshore wind and a subsequent enhancement in rainfall. The analysis points to a tendency towards more frequent and stronger variations in the water vapor flux convergence along the equator, which is associated with the increased precipitation in coastal Peru. [ABSTRACT FROM AUTHOR]

Predictability is an intrinsic limit of the climate system due to uncertainty in initial conditions and the chaotic nature of the atmosphere. Estimates of predictability together with calculations of current prediction skill are used to define the gaps in our prediction capabilities, inform future model developments, and indicate to stakeholders the potential for making forecasts that can inform their decisions. The true predictability of the climate system is not known and must be estimated, typically using a perfect model estimate from an ensemble prediction system. However, different prediction systems can give different estimates of predictability. Can we determine which estimate of predictability is most representative of the true predictability of the climate system? We test three metrics as potential indicators of the fidelity of predictability estimates in an idealized framework—the spread-error relationship, autocorrelation and skill. Using the North American multi-model ensemble re-forecast database, we quantify whether these metrics accurately indicate a model's ability to properly estimate predictability. It is found that none of these metrics is a robust measure for determining whether a predictability estimate is realistic for El Nino-Southern oscillation events. For temperature and precipitation over land, errors in the spread-error ratio are related to errors in estimating predictability at the shortest lead-times, while skill is not related to predictability errors. The relationship between errors in the autocorrelation and errors in estimating predictability varies by lead-time and region. [ABSTRACT FROM AUTHOR]