Modelling and forecasting the Indian Ocean Dipole

与大西洋和太平洋的近亲相比，印度洋的研究开发程度相当低，但其周边国家人口众多(仅印度就有10亿人)，并且正在经历相当大的经济发展(特别是在非洲)。印度洋偶极子(IOD)--印度洋西/东海面温度的一种暖/冷模式--在2019年是有记录以来最大的，被认为与那年夏天印度季风的强烈降雨、随后几个月史无前例的澳大利亚野火有关，并对整个东非特别是索马里和维多利亚湖盆地产生了显著影响。尽管印度洋及其气候多变性很重要，但该区域在天气和气候模式方面存在很大偏差，同时，为第六次耦合模式相互比较项目(CMIP6)作出贡献的新模式系列可供分析，该项目为气专委第六次评估报告提供了依据。CMIP6包括来自英国气象局的最先进的贡献。新的印度洋观测也可从系泊阵列、Argo浮标、实地活动和最新的再分析产品中获得。该项目将利用气象局独特的无缝建模方法，在不同的尺度上使用相同的核心模型进行预测，以检查气候模型中的印度洋偏差，这些偏差发展得有多快及其可能的来源，并测试这些偏差对印度、东非和海洋大陆气候影响的影响。在个案支持下，PHD提供了利用Met Office GloSea5季节性预报系统进行新试验的机会，该系统使人们能够容易地确定远程遥相关对印度洋偏差的敏感性。这些可能包括放松/轻推特定区域或海洋盆地的平均状态到观测到的气候或调查对ENSO状态的依赖。本项目旨在解决以下主要问题：第六次耦合模式相互比较项目(CMIP6)的模式如何表示印度洋的平均状态和变化性？如何在CMIP6模式中模拟印度洋偶极子的遥相关？印度洋的错误发展速度有多快，它们从哪里开始？这个问题将集中在气候模式版本的MetUM和耦合的初始化季节预报系统GloSea5.Q3之间的详细比较。本地和偏远海洋偏差对模拟印度洋区域气候影响的遥相关有什么影响？培训机会：通过这个项目，学生将发展强大的编程技能和管理大型数据集的能力，包括在NCAS的cf-python/cf-PLOT课程中为此进行的培训。该项目将在埃克塞特的气象局提供实习(例如每年一个月)，以发展使用MetUM模型的实践经验，向其开发人员学习，并提高模型工作流程和数据处理方面的技能。这也将使学生有机会讨论并向更广泛的受众展示他们的工作，并提供与领先的博士毕业生雇主接触的机会。学生将有机会参加由NCAS提供的大气科学概论课程和气候模型暑期学校的进一步培训。学生将拥有与情景博士学位相关的所有常见机会，如发展演讲技能、在国内和国际会议上建立关系网等。

The Indian Ocean is considerably underexplored for research compared to its Atlantic and Pacific cousins, yet the countries around its perimeter have large populations (one billion in India alone) and are undergoing considerable economic development (especially in Africa). The Indian Ocean dipole (IOD) - a pattern of warm/cold sea-surface temperatures in the west/east Indian Ocean - was the largest on record in 2019, thought have bearing on the strong Indian monsoon rainfall that summer, the unprecedented Australian wildfires of the following months, and with noted impacts across East Africa, especially in Somalia and the Lake Victoria Basin. Despite the importance of the Indian Ocean and its climate variability, the region suffers large biases in weather and climate models.Meanwhile, the new range of models contributing to the sixth coupled model intercomparison project (CMIP6), which inform the IPCC 6th Assessment Report are available for analysis. CMIP6 includes state-of-the-art contributions from the Met Office. New Indian Ocean observations are also available from moored arrays, Argo floats, field campaigns and the latest reanalysis products. This project will exploit the Met Office's unique seamless modelling approach, in which the same core model is used for forecasting at a variety of scales, in order to examine Indian Ocean biases in climate models, how quickly those biases develop and their likely sources, and testing the impact of those biases on climate impacts for India, eastern Africa and the Maritime Continent. With CASE support, the PhD presents the opportunity for novel experiments to be performed with the Met Office GloSea5 seasonal forecasting system, which allows the sensitivity of remote teleconnections to Indian Ocean biases to be readily determined. These could include relaxing/nudging the mean state in particular regions or ocean basins back to observed climatologies or investigating the dependence on the state of ENSO.This project aims to address the following major questions:Q1. How is the mean state and variability of the Indian Ocean represented in the models of the sixth coupled model intercomparison project (CMIP6)? How are teleconnections of the Indian Ocean dipole simulated in the CMIP6 models?Q2. How rapidly do errors in the Indian Ocean develop and where do they start? This question will focus on a detailed comparison between climate model versions of the MetUM and the coupled initialised seasonal forecasting system, GloSea5.Q3. What is the impact of local and remote ocean biases on the simulation of teleconnections to regional climate impacts from the Indian Ocean?Training opportunities:Through this project the student will develop strong programming skills and the ability to manage large datasets, including training for this in NCAS courses for cf-python/cf-plot. The project will offer placements at the Met Office, Exeter (e.g. one month per year) to develop hands-on experience working with the MetUM model, learning from its developers and improving skills in model workflows and data processing. This will also give the student the chance to discuss and present their work to a wider audience and presents the opportunity to engage with a leading employer of PhD graduates. The student will have the opportunity to undertake further training offered by NCAS in their Introduction to Atmospheric Science course and Climate Modelling Summer School. The student will have all the usual opportunities associated with a SCENARIO PhD such as developing presentation skills, networking at national and international conferences etc.