BECS: Decadal Climate Prediction Based on Coupled Ocean-Atmosphere Models and Networks of Aerial and Oceanic Sensors

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Decadal prediction focuses on time-evolving regional climate conditions over the next ten to thirty years with the focus on understanding how quickly the climate is changing and where the key changes will occur. Numerous assessments of climate information user needs have identified this timescale as being important to infrastructure planners, water resource managers, and many others. The ultimate task of climate science is to understand climate variations from realistic mathematical models and from data of atmospheric, oceanic, and satellite observations. Ocean and atmosphere circulations -- linked by the sea surface temperature(SST) -- are integral factors affecting the evolution of climate and a multitude of climate phenomena, such as the greenhouse effect, the polar ice cap melting, and global rainfall pattern changes. These phenomena in turn have implications in forming energy and national safety policies. The theoretical basis for such an investigation is far from complete. Our approach is to combine the information from the observations with the dynamics of coupled system in order to design an adaptive data ssimilation and initialization method that captures the complexity of Earth's climate and leads to reliable predictions. The main outcome of the proposal will be a theoretically sound basis for probabilistic climate prediction that will lead to the understanding Earth's climate system and the prediction of decadal climate variability. Our approach will be a cross-disciplinary one, spanning the spectrum from modeling and novel analytical techniques to numerical verification.Through this grant we will foster intensive training of engineering and applied mathematics undergraduate students, developing their motivation to pursue graduate studies in this multidisciplinary area. The educational broader impacts include the cross-disciplinary collaboration and training of graduate students. An integral part of the final outcome of the roposed work will be web-based simulators to be used in senior undergraduate and graduate courses and to provide the community a predictive tool that will enhance visual understanding of the effect of noise on climate models.