A systematic and extensive investigation of Indian Ocean Dipole predictability

印度洋偶极子(IOD)是热带印度洋显著的海-气耦合年际变化气候模式。它的特点是东、西印度洋海表温度异常相反，赤道印度洋中部海面风异常。由于印度洋盆地的SSTA梯度很强，IOD可以显著地调节大气环流，在世界许多地区引发一系列气候异常，包括印度洋周边地区、南美洲和东非等。作为全球气候总周期的一个方面，IOD与类似的现象如太平洋的厄尔尼诺和南方涛动(ENSO)相互作用，也可能影响加拿大的气候。IOD的研究开始得比ENSO晚得多。根据目前的气候模式评估，它的预测能力很低，落后于ENSO。因此，探索IOD预测能力低的原因是很重要的。它的低技能是由于模型错误、初始化错误，还是仅仅是IOD的内在可预测性？有可能提高IOD预测技能吗？如果可能，提高多少？这项提议旨在通过系统分析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。这将通过使用耦合的大气环流模式和最优扰动技术构建一个100年的长期集合后预报来实现，该技术使用耦合的数据同化方法进行初始化。通过分析长期的集合后播，可以很好地理解IOD的可预测性，包括它对季节(季节性障碍)和几十年的依赖。此外，应用最近发展的基于信息论的潜在可预测性框架，我们期望探索与IOD潜在可预测性相关的一些重要问题，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，从而产生重要和可靠的发现，这些发现将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重要的理论意义和实践意义，并将为培养高素质的气候模拟、预报和数据同化领域的人才做出重要贡献，这些领域目前在加拿大和世界范围内都很需要。

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.