A systematic and extensive investigation of Indian Ocean Dipole predictability

印度洋偶极子（IOD）是热带印度洋地区一种重要的海气耦合气候年际变化模态。其特征是东、西印度洋海温异常相反，赤道印度洋中部海面风场异常。由于印度洋海盆具有较强的海温异常梯度，印度洋海温异常对大气环流具有明显的调制作用，在印度洋周边地区、南美洲、东非等地诱发了一系列气候异常，作为全球气候一般周期的一个方面，与太平洋的厄尔尼诺和南方涛动（ENSO）等类似现象相互作用，IOD也可能影响加拿大的气候。IOD的研究比ENSO晚得多。用现有的气候模式评估，它的预测能力很低，落后于ENSO。因此，重要的是，探索IOD的预测能力低的原因。它的低技能是由于模型错误，初始化错误，或者仅仅是IOD的内在可预测性？是否有可能提高IOD预测技能，如果有，提高多少？** 本提案旨在通过系统分析IOD的实际预测技能和潜在可预测性来回答这些尚未得到很好解决的问题。这将通过使用耦合大气环流模式和最佳扰动技术构建100多年的长期集合后报来实现，并使用耦合数据同化方法进行初始化。IOD的可预测性，包括其对季节（季节性障碍）和几十年的依赖性，有可能通过分析长期的合奏后报。此外，应用最近发展的潜在的可预测性框架，基于信息论，我们希望探讨一些重要的问题与IOD潜在的可预测性，包括其定量估计和控制其变化所需的前兆。** 这项拟议的研究计划将提高对IOD可预测性的理解，从而产生重要而有力的发现，为世界上正在开发的业务预测系统提供理论和科学指导。本文提出的研究具有理论意义和实践意义，也将为培养高素质的人才在气候建模，预测和数据同化领域，目前在加拿大和世界各地的高需求作出重大贡献。

The Indian Ocean Dipole (IOD) is a prominent air-sea coupled climate mode of inter-annual variability in the tropical Indian Ocean. It is characterized by an opposite sea surface temperature anomaly (SSTA) in the eastern and western Indian Ocean, with the sea surface wind anomalies over the central equatorial Indian Ocean. Due to the strong SSTA gradient across the Indian Ocean basin, the IOD can significantly modulate the atmospheric circulation, inducing a series of climate anomalies in many areas of the world, including the surrounding area of the Indian Ocean, South America, and East Africa etc., As an aspect of the general cycle of global climate, interacting with similar phenomena like El Nino and Southern Oscillation (ENSO) in the Pacific Ocean, the IOD could also impact Canada's climate.******The IOD study began much later than ENSO. Its prediction skill, evaluated by current climate models, is very low and lags behind that of ENSO. It is important, therefore, to explore the reasons for the low prediction ability of the IOD. Is its low skill due to model errors, initialization errors, or simply the intrinsic predictability of IOD? Is it possible to improve the IOD prediction skill and, if so, by how much? ******This proposal aims to answer these questions, which have not yet been well addressed, by systematically analyzing the actual prediction skill and the potential predictability of the IOD. This will be implemented by constructing a long-term ensemble hindcast over 100 years using a coupled general circulation model and an optimal perturbation technique, initialized using the coupled data assimilation method. A good understanding of IOD predictability, including its dependence on seasons (seasonal barrier) and decades, is possible by analyzing the long-term ensemble hindcast. Further, applying the recently developed framework of potential predictability based on information theory, we expect to explore some important problems associated with the IOD potential predictability, including its quantitative estimate and the precursors needed to control its variation. ******This proposed program of research will improve understanding of the IOD predictability resulting in important and robust findings that will provide theoretical and scientific guidance to the operational prediction system being developed in the world. The research proposed herein has both theoretical importance and practical significance, and will also make significant contributions to the training of highly qualified personnel in climate modelling, prediction and data assimilation areas that are presently in high demand in Canada and worldwide.