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CIRCULATES - Circulation, Clouds and Climate Sensitivity

循环 - 循环、云和气候敏感性

基本信息

批准号：
NE/T006315/1
负责人：
Christopher Holloway
金额：
$ 68.46万
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：
2020
资助国家：
英国
起止时间：
2020 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=NE%2FT006315%2F1
关键词：
CIRCULATES Circulation Clouds Climate Sensitivity

项目摘要

Climate models are numerical models used to make projections of future climate change. Because of limitations in computing power, approximations to some parts of the model are required, particularly on small scales where important processes occur that are smaller than the model grid on which calculations are carried out. It is not clear how best to approximate small-scale processes and as a result, different GCMs use different approximations and produce different predictions of future climate change. One of the most important of these uncertainties is how low clouds are represented, and that is the focus of the CIRCULATES proposal.We now have access to new, high resolution satellite observations that we can use to build datasets that give us a much better idea of how clouds form and disperse, and how they interact with the environment in which they find themselves. We also have high resolution modelling tools that are able to represent the physical processes necessary to simulate clouds with much higher accuracy. High resolution models are far too computationally expensive to run for many model years over the whole globe in a way that could be used to project changes in climate directly. However, in conjunction with the satellite data, they can be used to determine the best way to represent the effects of clouds on the GCM model grid. This information can be transferred to the climate model, which can then be run to discover the impact of our findings on global climate change. In CIRCULATES, we propose to develop both new satellite data and high resolution simulations that are specifically designed to assist with improving and understanding the response of climate models with a focus on tropical and sub-tropical clouds.The project aims to assist the climate science and policy communities in two ways. First, the discoveries that we make will be used to assess the simulations made by climate models run by modelling centres around the world for the Intergovernmental Panel on Climate Change (IPCC) reports. How well are IPCC GCMs representing cloud processes in the present day? How does their representation change for simulations of the future and is this appropriate? By determining the fidelity of simulation in comparison with high resolution satellite and model data, we will determine the extent to which model simulations can be trusted, with the aim of constraining the likely range of future climate change. Second, we will develop metrics that are useful not only for constraining projections but also for model developers who are building the next generation of models. Our project has strong collaboration with the Met Office, who, together with the academic community, are the primary developers of models used for understanding climate change in the UK. We will engage with key Met Office and UKESM staff on a regular basis in order to determine how our results may be made most useful to model development.

气候模式是用来预测未来气候变化的数值模式。由于计算能力的限制，需要对模型的某些部分进行近似，特别是在发生重要过程的小尺度上，这些过程小于进行计算的模型网格。目前尚不清楚如何最好地近似小尺度过程，因此，不同的大气环流模型使用不同的近似值，对未来气候变化作出不同的预测。这些不确定性中最重要的一个是如何表示低云，这是CIRCULATES提案的重点。我们现在可以获得新的，高分辨率的卫星观测数据，我们可以使用这些数据来构建数据集，让我们更好地了解云如何形成和分散，以及它们如何与它们所处的环境相互作用。我们还拥有高分辨率的建模工具，能够以更高的精度表示模拟云所需的物理过程。高分辨率模式的计算成本太高，无法在整个地球仪上运行许多模式年，从而无法直接用于预测气候变化。然而，结合卫星数据，它们可以用来确定最好的方式来表示云对GCM模式网格的影响。这些信息可以被传输到气候模型中，然后可以运行该模型来发现我们的发现对全球气候变化的影响。在CIRCULATES项目中，我们建议开发新的卫星数据和高分辨率模拟，专门用于帮助改善和理解气候模型的响应，重点关注热带和亚热带云。该项目旨在以两种方式帮助气候科学和政策界。首先，我们的发现将用于评估世界各地的模型中心为政府间气候变化专门委员会（IPCC）报告运行的气候模型所做的模拟。IPCC GCM在当今代表云过程的程度如何？他们的代表如何改变未来的模拟，这是适当的？通过确定模拟与高分辨率卫星和模型数据相比的保真度，我们将确定模型模拟的可信度，目的是限制未来气候变化的可能范围。其次，我们将开发不仅对约束投影有用，而且对构建下一代模型的模型开发人员也有用的度量。我们的项目与气象局有着密切的合作，气象局与学术界一起，是用于了解英国气候变化的模型的主要开发者。我们将定期与英国气象局和UKESM的主要工作人员接触，以确定如何使我们的结果对模型开发最有用。