Why do weather and climate models get the Indian Ocean wrong?

为什么天气和气候模型对印度洋的预测结果是错误的？

• 批准号: 2730632
• 金额: --
• 依托单位: University of East Anglia
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2730632
• 关键词: Why; do; weather; climate; models

Scientific backgroundThe Indian Ocean is a key component of global climate, surrounded by monsoon systems on which billions of people depend, and warming faster than any other ocean basin. However, state-of-the-art climate models fail to accurately capture the dynamical and thermodynamical processes that govern climatic variability around the Indian Ocean. The UK Met Office has identified model errors and biases in this region to be a significant problem for making seasonal climate forecasts, yet little is known about the source of these errors or how they could be reduced. Research methodologyYou will identify the key processes that generate errors in the Met Office weather and climate models to identify potential model improvements. Initially you will compute the ocean surface mixed layer heat budget, which controls variability in sea-surface temperature and atmosphere-ocean interaction, and compare this budget against observations to identify errors. You will then extend this work to evaluate model experiments where the atmosphere and ocean are "nudged" towards observed values, to identify the role of different regions and components of the climate system in generating model errors and biases. Finally you will run short sensitivity studies to identify optimal model setups and pathways for future development.Aims and objectives1. Analysis of a mixed layer heat budget in seasonal forecasts of the Met Office Unified Model, following the methodology of Graham & Vellinga (2013), to separate the role of surface heat fluxes, horizontal and vertical advection, vertical mixing, vertical diffusion and mixed layer dynamics. This step enables the drivers of SST variability and biases to be identified and referenced against a range of in-situ observations, including atmosphere and ocean observations from moorings and publicly available data from several recent intensive field campaigns (e.g., Vijith et al., 2020). The student will quantify the extent to which erroneous representation of contributors to SST variability may contribute to the overall SST bias. 2. Analysis of nudged runs, where the atmosphere or ocean is nudged towards observed values in certain region (e.g., Rodriguez et al., 2017). This comparison will reveal the role of biases in the ocean dynamics in generating SST biases. Comparison of the dynamics with the freely coupled model will be used to reveal regions where there may be high sensitivity to ocean processes. Dynamical links between regions can be further investigated using regional ocean nudging over a set of locations to evaluate the remote impacts of local ocean biases. 3. Comparison of various modelling configurations to identify pathways for future model development. It is expected that the new GC5 coupled model configuration will be available for comparison with the earlier GC3 using existing simulations. The new OSMOSIS mixed-layer scheme (described in Damerell et al., 2020) is expected to be available during the project, which will provide an opportunity to evaluate its performance in the Indian Ocean. The student may also run short sensitivity studies in which parameters in either the existing (TKE) mixing scheme or OSMOSIS can be varied to identify potential avenues for further model development.

科学背景印度洋是全球气候的一个关键组成部分，被数十亿人赖以生存的季风系统所包围，并且比任何其他海洋盆地变暖都快。然而，最先进的气候模式无法准确捕捉到印度洋周围气候变化的动力学和热力学过程。英国气象局已经确定该地区的模型误差和偏差是季节性气候预测的一个重要问题，但对这些误差的来源或如何减少这些误差知之甚少。研究方法您将确定在气象局天气和气候模型中产生错误的关键过程，以确定潜在的模型改进。首先，您将计算海洋表面混合层热收支，它控制海洋表面温度和大气-海洋相互作用的变化，并将此预算与观测结果进行比较以确定错误。然后，您将扩展这项工作，以评估大气和海洋被“推向”观测值的模型实验，以确定气候系统的不同区域和组成部分在产生模型误差和偏差中的作用。最后，您将进行简短的敏感性研究，以确定最佳的模型设置和未来发展的途径。在气象局统一模式的季节预报中分析混合层热量收支，遵循Graham & Vellinga（2013）的方法，以区分地表热通量、水平和垂直平流、垂直混合、垂直扩散和混合层动力学的作用。这一步骤使SST变异性和偏差的驱动因素得以确定，并参照一系列现场观测，包括系泊的大气和海洋观测以及最近几次密集实地活动的公开数据（例如，Vijith等人，2020年）。学生将量化SST变异性贡献者的错误表示可能导致总体SST偏倚的程度。2.分析微推运行，其中大气或海洋被微推到某些区域的观测值（例如，Rodriguez等人，2017年）。这种比较将揭示海洋动力学中的偏差在产生SST偏差中的作用。动态与自由耦合模式的比较将用于揭示可能对海洋过程高度敏感的区域。区域之间的动态联系可以通过在一系列地点进行区域海洋轻推来进一步研究，以评估当地海洋偏差的远程影响。3.比较各种建模配置，以确定未来模型开发的途径。预计新的GC 5耦合模型配置将可用于与使用现有模拟的早期GC 3进行比较。新的OSMOSIS混合层方案（描述于Damerell等人，2020年）预计将在项目期间提供，这将提供一个机会来评估其在印度洋的表现。学生还可以进行短期敏感性研究，其中现有（TKE）混合方案或OSMOSIS中的参数可以变化，以确定进一步模型开发的潜在途径。