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)

• 批准号: NE/G006148/1
• 负责人: Philip Stier
• 金额: $ 36.7万
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2009
• 资助国家: 英国
• 起止时间: 2009 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FG006148%2F1
• 关键词: AErosol; model; RObustness; Sensitivity; study

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.

AEROS是由利兹大学、牛津大学、英国气象局和欧洲气象和气象研究中心合作建立的，目的是全面评估最新一代全球气溶胶模型的性能，量化不确定性，并制定改进战略。气溶胶对气候具有重要但非常不确定的影响（IPCC，2007）。不确定性主要来自模型间的差异，必要的简化气溶胶过程的计算成本的原因，以及用于模型评估的观测的不确定性。复杂的“下一代”气溶胶微物理方案最近已开发的几个气候模式，旨在提高模型的现实性和提高预测的可靠性。该模型解决颗粒大小和各种化学成分，并使用一个完整的微观物理方案，包括成核，凝聚，尺寸分辨沉积，云处理等先进的气溶胶模型的发展创造了新的和实质性的挑战，本建议旨在解决。首先，复杂的气溶胶模型的计算需求意味着不确定性分析技术还没有被常规使用，所以我们只有很少的信息来指导模型的改进（模型因子的不确定性重要性，结构与参数不确定性的相对重要性等）。我们将使用敏感性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气候模型运行时间的很大一部分，因此客观地评估模型复杂程度至关重要，以便优先考虑和优化未来的发展。以前的模型评估没有回答模型是否比要求的更复杂或更简单的问题，或者应该在哪里投入开发努力。这个建议的一个重要方面是量化模型的解释能力与复杂性，这可能是规模依赖。在已经昂贵的模型部分中找到适当的复杂程度的好处将是巨大的：更多和更长的模型运行，更多的气候敏感性测试等。但大多数微物理量（如颗粒数，尺寸分辨成分等）只能通过飞机和地面站相当局部化的原位技术进行测量。稀疏的测量限制了模型评估的许多方面的案例研究，而不是长期的平均测量中使用的以前的评估，如AeroCom。因此，目前这一代气溶胶模型已经评估了一小部分可用的微物理观测。在这个项目中，我们的目标是通过利用2008年5月进行的EUCAARI和EMEP密集活动的观测来克服这个问题。通过综合密集观测，我们的目标是预测量之间的一致性，并避免使用单个数据集时出现的补偿模型因子的问题。AeroCom国际气溶胶相互比较项目非常成功地记录了模拟气溶胶的最新技术水平。它汇集了大多数全球气溶胶模型的观测结果和结果，以评估我们对全球气溶胶影响的理解。然而，由于很难在广泛的模型中建立可比较的诊断，因此很难将结果的差异归因于特定的过程。我们的方法将评估模型的过程水平，并评估其性能对微观物理观测的第一次。这一建议的总体成果将是改进对气溶胶特性、变异性和空间分布的预测，这些是准确预测气溶胶气候和空气质量影响的基本要求。

项目成果

Constraints on aerosol processes in climate models from vertically-resolved aircraft observations of black carbon

垂直分辨飞机观测黑碳对气候模型中气溶胶过程的限制

• DOI: 10.5194/acpd-13-437-2013
• 发表时间: 2013
• 作者: Kipling Z

The contribution of the strength and structure of extratropical cyclones to observed cloud-aerosol relationships

温带气旋的强度和结构对观测到的云-气溶胶关系的贡献

• DOI: 10.5194/acp-13-10689-2013
• 发表时间: 2013
• 期刊: Atmospheric Chemistry and Physics
• 影响因子: 6.3
• 作者: Grandey B

Assessment of black carbon radiative effects in climate models

• DOI: 10.1002/wcc.180
• 发表时间: 2012-07
• 期刊: Wiley Interdisciplinary Reviews: Climate Change
• 作者: J. Feichter;P. Stier

Corrigendum to "The magnitude and causes of uncertainty in global model simulations of cloud condensation nuclei" published in Atmos. Chem. Phys., 13, 8879-8914, 2013

更正

• DOI: 10.5194/acp-13-9375-2013
• 发表时间: 2013
• 期刊: Atmospheric Chemistry and Physics
• 影响因子: 6.3
• 作者: Lee L

MAC-v1: A new global aerosol climatology for climate studies

• DOI: 10.1002/jame.20035
• 发表时间: 2013-12-01
• 期刊: JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS
• 影响因子: 6.8
• 作者: Kinne, Stefan;O'Donnel, Declan;Stevens, Bjorn
• 通讯作者: Stevens, Bjorn