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

科学背景印度洋是全球气候的重要组成部分，被数十亿人赖以生存的季风系统包围，而且变暖的速度比其他任何洋盆都快。然而，最先进的气候模型未能准确地捕捉到支配印度洋周围气候变异性的动力和热力学过程。英国气象局已经发现，该地区的模型误差和偏差是做出季节性气候预测的一个严重问题，但人们对这些误差的来源或如何减少这些误差知之甚少。研究方法你将确定在气象局天气和气候模式中产生误差的关键过程，以确定潜在的模式改进。最初，您将计算海洋表面混合层热量收支，它控制海-表面温度和大气-海洋相互作用的可变性，并将该收支与观测值进行比较，以找出误差。然后，您将扩展这项工作，以评估大气和海洋向观测值“推”的模型实验，以确定气候系统的不同区域和组件在产生模型误差和偏差方面的作用。最后，您将运行短期敏感度研究，以确定未来发展的最佳模型设置和路径。按照Graham&Vellinga(2013)的方法，在气象局统一模式的季节预报中对混合层热量收支的分析，以分离地面热通量、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。这一步骤使得能够根据一系列现场观测，包括来自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)来确定和参照SST可变性和偏差的驱动因素。学生将量化对SST可变性的贡献者的错误表述可能在多大程度上导致总体SST偏差。2.对微动运行的分析，即大气或海洋被推向某些区域的观测值(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。动力学与自由耦合模式的比较将被用来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组地点上的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.比较各种模型配置，以确定未来模型开发的路径。预计新的GC5耦合模式配置将可用于使用现有模拟与较早的GC3进行比较。新的渗透混合层计划(Damerell等人，2020年所述)预计将在项目期间提供，这将为评估其在印度洋的表现提供机会。学生也可以进行短期的敏感性研究，在这些研究中，现有的(TKE)混合方案或渗透作用中的参数可以改变，以确定进一步开发模型的潜在途径。

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.