The dynamics and multi-year predictability of La Nina

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La Niña, a recurrent cooling pattern over the tropical Pacific Ocean, has been linked to reduced wintertime precipitation across the southern tier of the United States. Historical observations show that La Niña events can last two years or longer, a feature that could make their associated drought impacts more persistent. Forecast systems are generally able to predict the onset of La Niña, as they virtually always follow El Niño. However, little is known about the predictability of multi-year La Niña events because current operational ENSO forecasts are limited to 8 months. This project addresses a series of questions that are critical to improve our ability to predict these events. The project will determine the processes, initial ocean states, and models that can produce skillful multi-year predictions of tropical Pacific. Predicting whether La Niña will return for a second year is critical for predicting the duration of associated droughts throughout the world. Results from this project can potentially improve our ability to predict both the strength and duration of US droughts caused by La Niña.Observations show that La Niña events tend to last for an additional year, causing persistent drought and flooding impacts in regions throughout the world. Forecasts of these 2-year La Niña events are not routinely generated by operational prediction systems because they focus on shorter lead times, typically up to eight months. This project will demonstrate the feasibility of skillful predictions of 2-year La Niña. Multi-year forecasts will be performed using the Community Earth System Model Version 1 (CESM1), a model that simulates realistic and highly predictable 2-year La Niña. Model dependence of 2-year predictability will be explored using decadal climate predictions performed by four climate models that participated in Phase 5 of the Coupled Model Intercomparison Project (CMIP5). These retrospective forecasts (hindcasts) performed with CESM1 and CMIP5 will be used to explore the physical processes responsible for the predictability of historical 2-year La Niña. Advanced diagnostics will be used to attribute the processes causing forecast spread. Discrepancies in the hindcasts of historical 2-year La Niña events relative to their observed trajectory will be analyzed to identify their predictable and unpredictable drivers. An existing suite of seasonal predictions performed with CESM1 will be extended to produce 2-year hindcasts initialized in March, June, and September. Together with the existing suite of decadal predictions initialized in November, the proposed extension will be used to explore the impact of different lead times, initial state, and seasonality.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.