Controls on Southern African Wet Season Timing under Climate Change

南部非洲的经济、农村人口和经济发展高度依赖降雨量，降雨量在季节内到数十年的时间尺度上差异很大(Cook和Reason，2004年；Tadross，Hewitson和Usman，2005年；Conway等人，2015年)。该区域还非常容易受到与气候有关的风险的影响，如干旱和洪水以及气候变化，但由于次大陆的社会经济地位，准备或应对这些风险的能力有限(Dunning，Black和Allan，2018年)。这项研究旨在了解南部非洲雨季为什么以及如何在气候变化下变得更晚，以及CAB在这一过程中扮演着什么角色。DPHIL将通过论文路线完成，因此目标和方法分为三篇论文。第一篇论文将重点了解导致驾驶室故障的关键过程，包括再分析数据、历史GCM模拟和更高分辨率的模型。这是因为更好地了解驾驶室故障的原因将有助于更好地了解它在气候变化下可能对雨带运动产生的影响。首先，将分析三个再分析数据集--ERA-5、ERA-中期和MERRA2，以了解驾驶室故障是如何发生的，以及涉及哪些大气过程。驾驶室位置将使用Howard和Washington(2019)的方法确定，并将使用相对湿度、比湿度、地面风、温度和大气压力等数据场。然后，将在一大组CMIP6模型的历史模拟中重复分析，以便与再分析结果进行比较，以了解模型在捕捉驾驶室故障特征和故障过程方面的情况。第三，将在高分辨率区域模式中对关键过程进行评估，看看它们是否能更详细地揭示这些过程的特征。DPhil的下一个研究重点和论文将是评估影响驾驶室故障的关键过程在CMIP6模式的气候变化模拟中如何发生，以及这对雨带时间的影响。这将考验(S)驾驶室故障与气候变化下雨带移动延迟有何联系。首先，将计算历史和未来CMIP6模型运行的雨带特征及其时间，以了解雨带的时间如何变化。下一步是将模型分为雨带定时变化最大和变化较小的模型，并评估每个模型中导致驾驶室故障的关键过程。这可能需要评估相对湿度、湿度、地面风、气压和温度数据场。本文将在未来的气候模拟中使用RCP8.5/SSP585来最大化任何变化信号。随着DPhil的工作开始，第三篇论文的范围和重点可能会转移，这取决于论文1和2中发现的有趣发现。然而，本文的两个想法包括在理想化实验中评估驾驶室故障关键过程，或评估雨带定时变化对温室气体排放的敏感性。由于CMIP6集合中的模型种类繁多，很可能很难在第二篇论文中找到明确的因果关系，所以在气候变化下驾驶室故障关键过程和雨带时间之间的理想化实验可以澄清这些变量之间的关系。后一种想法将是一份更注重政策的文件，调查南部非洲作物的气候需求，以及在CMIP6模型中不同的SSP排放情景下何时不再满足这些需求。

Southern Africa's economy, rural population and economic development are highly dependent on rainfall, which varies significantly on intraseasonal to multidecadal timescales (Cook and Reason, 2004; Tadross, Hewitson and Usman, 2005; Conway et al., 2015). The region is also very vulnerable to climate-related risks, such as drought and flooding, and climate change, yet has limited ability to prepare or react to these due to the socio-economic status of the subcontinent (Dunning, Black and Allan, 2018). This research aims to understand why and how the Southern African rainfall season becomes later under climate change, and what role the CAB has in this process. The DPhil will be done through the paper route, so the objectives and methods are separated into three separate papers.The first paper will focus on understanding what key process(es) cause CAB breakdown, both in reanalysis data, historical GCM simulations and higher resolution models. This is because a better understanding of the causation of CAB breakdown will enable better understanding of the influence it may have on rain belt movement under climate change. Firstly, three reanalysis datasets - ERA-5, ERA-Interim and MERRA2 - will be analysed to understand how CAB breakdown occurs and what atmospheric processes are involved. CAB locations will be identified using the method of Howard and Washington (2019), and the data fields of relative humidity, specific humidity, surface winds, temperature and atmospheric pressure will be used. The analysis will then be repeated in the historical simulations of a large group of CMIP6 models, to enable comparison with the reanalysis results to see how well the models are capturing CAB breakdown characteristics and the process of breakdown. Thirdly, the key process(es) will be evaluated in high resolution regional models, to see if they can reveal the characteristics of these processes in more detail.The next research focus and paper of this DPhil will be an evaluation of how the key process(es) influencing CAB breakdown occur in climate change simulations of CMIP6 models, and what influence this has on rain belt timing. This will test in what way(s) the CAB breakdown is connected to the delayed movement of the rain belt under climate change. To begin, rain belt characteristics and their timing will be calculated for both historical and future CMIP6 model runs, in order to see how the timing of the rain belt changes. The next step is to separate the models into those with the greatest and smaller rain belt timing changes, and evaluate the key process(es) in CAB breakdown in each of them. This will likely require an evaluation of relative humidity, moisture, surface winds, pressure, and temperature data fields. This paper will use RCP8.5/SSP585 in the future climate simulations in to maximize any signal of change.As work on the DPhil begins, the scope and focus of the third paper is likely to shift, depending on the interesting discoveries found in papers 1 and 2. However, two ideas for this paper include evaluating CAB breakdown key processes in idealized experiments, or assessing the sensitivity of rain belt timing changes to greenhouse gas emissions. It is likely to be difficult to find a clear causality in the second paper due to the great variety of models in the CMIP6 ensemble, so idealized experiments between the CAB breakdown key processes and rain belt timing under climate change could clarify the relationships between these variables. The latter idea would be a more policy-focused paper, investigating the climatic requirements of Southern African crops and when these are no longer met under different SSP emissions scenarios in CMIP6 models.