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

年代际预测侧重于未来10至30年随时间演变的区域气候条件，重点是了解气候变化的速度以及关键变化将发生在哪里。对气候信息用户需求的大量评估表明，这一时间尺度对基础设施规划者、水资源管理者和其他许多人都很重要。气候科学的最终任务是从现实的数学模型和大气，海洋和卫星观测数据中了解气候变化。海洋和大气环流-由海洋表面温度联系在一起-是影响气候演变和温室效应、极地冰盖融化和全球降雨模式变化等多种气候现象的不可或缺的因素。这些现象反过来又对能源和国家安全政策的制定产生影响。 这种调查的理论基础还远远没有完成。我们的方法是联合收割机的观测信息与耦合系统的动力学，以设计一个自适应的数据同化和初始化方法，捕捉地球气候的复杂性，并导致可靠的预测。该提案的主要成果将是为概率气候预测奠定坚实的理论基础，从而有助于了解地球气候系统和预测十年气候变异。我们的方法将是一个跨学科的，跨越从建模和新的分析技术，以数值验证的频谱。通过这笔赠款，我们将促进工程和应用数学本科生的强化培训，发展他们的动机，追求研究生学习在这个多学科领域。更广泛的教育影响包括跨学科的合作和研究生的培训。最终成果的一个组成部分将是基于网络的模拟器，用于高年级本科生和研究生课程，并为社会提供一个预测工具，将提高视觉上的理解噪声对气候模型的影响。

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.