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Improving simple climate models through a traceable and process-based analysis of ocean heat uptake in AOGCMs and observations

通过对 AOGCM 和观测中的海洋吸热进行可追溯和基于过程的分析，改进简单的气候模型

基本信息

批准号：
NE/K016083/1
负责人：
Remi Tailleux
金额：
$ 46.89万
依托单位：
University of Reading
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2013
资助国家：
英国
起止时间：
2013 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=NE%2FK016083%2F1
关键词：
Improving simple climate models through

项目摘要

Future climate change predictions provide essential guidance for the global efforts to curb the global warming trend caused by human emissions of greenhouse gases. The main tool to provide these projections are coupled atmosphere-ocean general circulation models (AOGCMs). However, these models are computationally still very expensive to run. Therefore, simple climate models (SCMs) have been developed, which are able to mimic the climate response seen in the AOGCMs, but at a much reduced computational cost. SCMs are being used for several purposes, e.g. simulating how the projections depend on key climate parameters, or for the interpretation of the AOGCM projections. SCMs are often used for policy advice.To ascertain the usefulness and accuracy of SCMs, it is essential to establish their traceability to comprehensive AOGCMs and ultimately to reality. The link and hence the traceability between SCMs, AOGCMs and observations is established through the process of calibration, which is the key step whereby the values of the control parameters of a given model are set up. There appear to be two main kinds of calibration: physical and behavioural, which operate very differently. A physical calibration is one that specifies the control parameters of a given model by invoking physical arguments or observational constraints on the physical processes involved. In contrast, a behavioural calibration is one that specifies the same control parameters so that the model reproduces various emergent properties of the actual or simulated climate system, under past, present or future conditions. Because climate change is primarily controlled by ocean heat uptake (OHU), the accuracy of climate change projections depends on the validity of the representation of the physical processes controlling OHU in SCMs and AOGCMs. How these processes are represented varies widely across SCMs, and ranges from explicit to entirely implicit representations. In this project, three specific SCMs are considered. In MAGICC, ocean heat uptake is explicitly represented through two vertical advection/diffusion equations for each hemisphere. In Gregory (2000)'s two-layer model, it is represented via two simple ordinary differential equations with two-time scales. In Good et al. (2011,2012)'s step-response approach, ocean heat uptake is entirely implicit. In this proposal, our first objective will be to assess the degree of generality of each SCM by testing their relative performances on the same range of climate change scenarios, in order to identify which simplified representation of OHU performs better. Our second objective will be to investigate the link between physical and behavioural calibration, by implementing a physical calibration of MAGICC, and testing whether it improves or deteriorates its performances and why. This will provide key new insights on which aspects of ocean heat uptake are robust, and which are in need of further study, which will enhance the credibility of SCMs. The first and second objectives address Goal 2 of the Call. Our third objective will be to develop a versatile and flexible approach to constraining ocean heat uptake processes by using NEMO and its adjoint NEMOTAM, allowing for the optimal calibration of mixing parameters from both physical and behavioural constraints. This will endow the UK with a key capability for systematically re-calibrating ocean models by incorporating observational and theoretical advances on ocean mixing processes, and testing the implications for climate change projections. Moreover, the optimisation set-up for NEMO is potentially useful for many other types of studies in the future. This addresses Goal 1 of the Call. This will make a key contribution to the joint NERC-Met Office strategy for the development of UKESM1.

未来气候变化预测为全球努力遏制由人类排放的温室气体造成的全球变暖趋势提供了重要指导。提供这些预测的主要工具是大气-海洋耦合环流模式(AOGCM)。然而，这些模型在计算上运行起来仍然非常昂贵。因此，已经开发了简单的气候模型(SCM)，它能够模拟AOGCM中看到的气候响应，但计算成本大大降低。SCMS被用于几个目的，例如模拟预测如何依赖于关键的气候参数，或用于解释AOGCM预测。SCM经常用于政策建议。为了确定SCM的有效性和准确性，必须建立它们对全面的AOGCMS的可追溯性，并最终达到现实。SCM、AOGCM和观测值之间的联系和可追溯性是通过定标过程建立的，这是建立给定模型的控制参数值的关键步骤。似乎有两种主要的校准方式：生理校准和行为校准，它们的操作方式截然不同。物理校准是通过引用物理参数或对所涉及的物理过程的观测约束来指定给定模型的控制参数的校准。相比之下，行为校准是指指定相同的控制参数，以便模型在过去、现在或未来的条件下再现实际或模拟的气候系统的各种紧急特性。由于气候变化主要由海洋热吸收(OHU)控制，气候变化预测的准确性取决于SCM和AOGCM中控制OHU的物理过程表示的有效性。这些流程的表示方式在不同的SCM中差别很大，从显式表示到完全隐式表示。在这个项目中，考虑了三个具体的供应链管理。在MAGICC中，海洋热吸收通过每个半球的两个垂直平流/扩散方程显式表示。在Gregory(2000)的S二层模型中，用两个简单的双时间尺度的常微分方程组来表示。在Good等人中。(2011,2012)S的阶跃响应方法，海洋热吸收是完全隐式的。在这项提案中，我们的第一个目标将是通过测试每种SCM在相同范围的气候变化情景下的相对表现来评估每种SCM的一般性程度，以确定OHU的哪个简化表示法表现得更好。我们的第二个目标将是通过实施MAGICC的物理校准来调查物理校准和行为校准之间的联系，并测试它是改善还是降低其性能及其原因。这将提供关于海洋热量吸收的哪些方面是稳健的，哪些需要进一步研究的关键新见解，这将提高SCM的可信度。第一个和第二个目标涉及电话会议的目标2。我们的第三个目标将是开发一种通用和灵活的方法，通过使用NEMO及其附属的NEMOTAM来限制海洋热吸收过程，从而允许从物理和行为限制中对混合参数进行最佳校准。这将赋予英国一项关键能力，通过纳入海洋混合过程的观测和理论进步，并测试对气候变化预测的影响，系统地重新校准海洋模型。此外，NEMO的优化设置可能对未来许多其他类型的研究有用。这解决了电话会议的目标1。这将对英国国家气象局-气象局联合发展战略作出重要贡献1。