AErosol model RObustness and Sensitivity study for improved climate and air quality prediction (AEROS)

AErosol 模型 RObustness 和灵敏度研究，用于改进气候和空气质量预测 (AEROS)

基本信息

批准号：
NE/G006172/1
负责人：
Kenneth Carslaw
金额：
$ 43.01万
依托单位：
University of Leeds
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2010
资助国家：
英国
起止时间：
2010 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=NE%2FG006172%2F1
关键词：
AErosol model RObustness Sensitivity study

项目摘要

AEROS is a collaboration of the University of Leeds, Oxford University, the UK Met Office and EMEP to comprehensively assess the performance, quantify the uncertainties and develop strategies for improvements of the latest generation of global aerosol models. Aerosols have an important but very uncertain impact on climate (IPCC, 2007). The uncertainty derives primarily from inter-model differences, the necessary simplification of aerosol processes for computational cost reasons, and uncertainties in the observations used for model evaluation. Complex 'next generation' aerosol microphysics schemes have recently been developed for several climate models that are intended to enhance model realism and improve the reliability of predictions. The models resolve particle sizes and various chemical components, and use a full microphysics scheme including nucleation, coagulation, size-resolved deposition, cloud processing, etc. The development of such advanced aerosol models creates new and substantial challenges that this proposal aims to address. Firstly, the computational demands of complex aerosol models mean that techniques of uncertainty analysis have not been routinely used, so we have very little information to guide model improvement (uncertainty importance of model factors, relative importance of structural versus parameter uncertainty, etc). We will use sensitivity and uncertainty analysis techniques to identify the most important model improvements required. Secondly, because aerosol models already consume a large fraction of climate model run-time, it is vital to assess the level of model complexity objectively so as to prioritise and optimise future development. Previous model assessments have not answered the question of whether models are more or less complex than required or where development effort should be invested. An important aspect of this proposal is the quantification of model explanatory power versus complexity, which may be scale-dependent. The benefits of finding an appropriate level of complexity in an already expensive part of the model will be enormous: more and longer model runs, more climate sensitivity tests, etc. Thirdly, more complex models require evaluation against equally information-rich datasets. But most microphysical quantitites (such as particle number, size-resolved composition, etc) can only be measured with fairly localised in situ techniques from aircraft and from ground stations. The sparse measurements restrict many aspects of model evaluation to case studies rather than long-term average measurements used in previous evaluations such as AeroCom. So the present generation of aerosol models have been evaluated against a tiny fraction of available microphysics observations. In this project we aim to overcome this problem by exploiting observations from the EUCAARI and EMEP intensive campaigns conducted in May 2008. By synthesising intensive observations we will aim for consistency among predicted quantities and avoid the problem of compensating model factors that arises when single datasets are used. The AeroCom international aerosol intercomparison project has been very successful in documenting the state-of-the-art of the simulated aerosol. It has assembled observations and results from the majority of global aerosol models to assess our understanding of global aerosol effects. However, the difficulty of establishing comparable diagnostics across a wide range of models has made it difficult to attribute differences in the results to specific processes. Our approach will assess the models at the processes level and evaluate their performance against microphysics observations for the first time. The overall outcome of this proposal will be improvement in predictions of aerosol properties, variability and spatial distribution that are fundamental requirements for accurate prediction of aerosol climate and air quality effects.

爱神（ERO）是利兹大学，牛津大学，英国会议办公室和EMEP的合作，以全面评估绩效，量化不确定性并制定策略，以改善最新一代的全球气溶胶模型。气溶胶对气候有重要但非常不确定的影响（IPCC，2007年）。不确定性主要来自模型间差异，出于计算成本原因的必要简化气溶胶过程以及用于模型评估的观察结果的不确定性。最近已经开发了几种旨在增强模型现实主义并提高预测可靠性的气候模型的复杂“下一代”气溶胶微物理方案。这些模型可以解决粒径和各种化学成分，并使用完整的微观物理学方案，包括成核，凝结，大小分辨沉积，云处理等。这种先进的气溶胶模型的开发会带来该提案旨在解决的新的和实质性的挑战。首先，复杂的气溶胶模型的计算需求意味着未常规使用不确定性分析的技术，因此我们几乎没有信息来指导模型改进（不确定性的重要性，模型因素的重要性，结构性不确定性与参数不确定性的相对重要性等）。我们将使用灵敏度和不确定性分析技术来确定所需的最重要的模型改进。其次，由于气溶胶模型已经消耗了大量的气候模型运行时间，因此客观地评估模型复杂性的水平至关重要，以便优先级和优化未来的发展。以前的模型评估没有回答一个问题，即模型是否比要求或应投入开发工作的地方更复杂。该提案的一个重要方面是量化模型解释力与复杂性的量化，这可能依赖于规模。在模型中本来就已经昂贵的部分中找到适当的复杂性的好处将是巨大的：越来越长的模型运行，更多的气候灵敏度测试等。第三，更复杂的模型需要针对同样信息丰富的数据集进行评估。但是，大多数微物理定量材料（例如粒子数，尺寸分辨的组合物等）只能通过飞机和地面站的相当局部的原位技术来测量。稀疏测量将模型评估的许多方面限制为案例研究，而不是先前评估（例如Aerocom）中使用的长期平均测量值。因此，已经针对一小部分可用的微物理学观测来评估了现代的气溶胶模型。在该项目中，我们的目标是通过利用2008年5月进行的Eucaari和EMEP强化活动的观察结果来克服这一问题。通过合成密集的观察，我们将旨在在预测数量中保持一致性，并避免使用单个数据集时出现的补偿模型因素的问题。 Aerocom International Aerosol比较项目在记录模拟气溶胶的最新技术方面非常成功。它已经组装了观察结果和大多数全球气溶胶模型的结果，以评估我们对全球气溶胶效应的理解。但是，在广泛的模型中建立可比诊断的困难使得很难将结果差异归因于特定过程。我们的方法将在过程水平上评估模型，并首次评估其针对微物理学观察的性能。该提案的总体结果将改善气溶胶特性，可变性和空间分布的预测，这是准确预测气溶胶气候和空气质量效应的基本要求。