OCES Seminar: Advancing Seasonal-to-Decadal Climate Prediction in the Tropical Pacific
Ocean-atmosphere interactions in the tropical Pacific are powerful drivers and predictors of global climate across various timescales, impacting water, land, and ecosystem resources. Here, I will present our recent progress in improving prediction skill for the tropical Pacific climate from seasonal to decadal timescales. On seasonal to interannual timescales (~1–12 months), forecasts of the El Niño-Southern Oscillation (ENSO) phenomenon serve as the foundation for seasonal climate outlooks worldwide. Many ENSO events can last for two to three years, prolonging and exacerbating their climate impacts. However, operational seasonal forecasts are limited to one-year lead times and cannot predict these multiyear ENSO events. To address this limitation, we have developed a multiyear climate forecast system that can skillfully predict the duration of ENSO events with lead times of up to two years. The second part of the presentation will focus on decadal (1–10 years) climate variations in the tropical Pacific, which have been poorly predicted by current climate models. I will show that the low decadal prediction skill in the tropical Pacific is attributable to model errors in simulating the tropical Pacific climate response to volcanic eruptions. In contrast, the prediction system excluding volcanic forcing exhibits high skill due to the predictability provided by slow oceanic processes. Finally, I will briefly discuss future work on expanding the Pacific prediction skill to the broader Earth System, along with strategies for addressing challenges related to model biases and observational uncertainties.
Dr. Xian Wu is a Postdoctoral Research Associate at Princeton University and the National Oceanic and Atmospheric Administration (NOAA) Geophysical Fluid Dynamics Laboratory (GFDL). She received her Ph.D. from the University of Texas at Austin in 2020 and her B.S. degree from Nanjing University of Information Science and Technology in 2015. Dr. Wu’s research interests include ocean-atmosphere interactions, climate variability and change, and climate predictions on seasonal to decadal timescales. She has developed initialized forecast systems using numerical Earth System Models to understand the physical processes governing the predictability and prediction skill of natural climate modes. Her current research focuses on understanding and correcting model biases, using a hierarchy of simulations with varying complexity, resolutions, and observational constraints.