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Quantifying and Reducing Uncertainty in the Processes Controlling Tropospheric Ozone and OH

量化和减少对流层臭氧和 OH 控制过程中的不确定性

基本信息

批准号：
NE/N003411/1
负责人：
James Oliver Felix Wild
金额：
$ 56.62万
依托单位：
Lancaster University
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2016
资助国家：
英国
起止时间：
2016 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=NE%2FN003411%2F1
关键词：
Quantifying Reducing Uncertainty Processes Controlling

项目摘要

Understanding the behaviour of hydroxyl (OH) radicals in the troposphere is vital for explaining and predicting atmospheric composition change and its impacts on air quality and climate. The observed atmospheric abundance of ozone and methane has increased substantially over the past century due to human activity, and the fates of these gases are strongly coupled through the short-lived OH radical. However, we do not currently understand the relative importance of the different processes and variables that govern the abundance of these gases. State-of-the-art global chemistry-climate models show differences in methane lifetime of almost a factor of two, preventing them from simulating realistically the observed atmospheric build-up of methane or correctly attributing its causes. These models are also unable to reproduce ozone observations from the late 19th century, or more recent ozone trends observed over the past two decades.This project addresses these weaknesses by using novel statistical approaches to quantify the sensitivity of OH, O3 and CH4 in global models to the processes and inputs that govern them, and by developing new observational constraints to reduce this uncertainty. We will apply tried and tested emulation methods to reproduce the response of computationally-expensive atmospheric models and to permit a more complete and quantitative assessment of process contributions to uncertainty in trace gas abundance. A unique aspect of this project is that we will apply this approach to five different global models to provide a robust assessment of model responses and to identify the cause of model differences for the first time. Our feasibility studies have successfully demonstrated the effectiveness and value of this approach. Using atmospheric composition measurements we will then develop new multi-variable observational constraints that allow us to reduce the uncertainty in key processes by applying Generalised Likelihood Uncertainty Estimation methods in this field for the first time.Using these constraints, we will quantify the contribution from changing emissions and climate to changes in O3 and CH4 since the preindustrial era. This will permit the first clear source attribution for changes in radiative forcing from O3 and CH4, informing future IPCC assessments. We will identify the factors required to match observed trends, allowing us to explain why current models fail to reproduce observations. We will apply the same techniques to propagate uncertainties in our understanding of processes and emissions to provide formal uncertainties in projected future O3 and CH4 for given emission pathways. This new analysis approach is timely and benefits greatly from our involvement in the international Chemistry-Climate Model Intiative (CCMI) multi-model assessment of past and future atmospheric composition change, allowing us to explain the diversity of model results and to reduce uncertainty in the resulting projections of atmospheric change.

了解氢氧自由基在对流层中的行为对于解释和预测大气成分变化及其对空气质量和气候的影响至关重要。在过去的世纪，由于人类活动，观测到的臭氧和甲烷的大气丰度大幅增加，这些气体的命运通过短寿命的OH自由基强烈耦合。然而，我们目前还不了解控制这些气体丰度的不同过程和变量的相对重要性。最先进的全球化学-气候模型显示，甲烷寿命的差异几乎是两倍，这使它们无法真实地模拟所观察到的大气甲烷积聚或正确地归因于其原因。这些模式也无法重现19世纪后期的臭氧观测结果，或在过去20年中观测到的最近的臭氧趋势。该项目通过使用新的统计方法来量化全球模式中OH、O3和CH 4对控制它们的过程和输入的敏感性，并通过开发新的观测约束来减少这种不确定性，从而解决这些弱点。我们将采用久经考验的仿真方法来重现计算昂贵的大气模型的响应，并允许一个更完整的和定量的评估过程中的微量气体丰度的不确定性的贡献。该项目的一个独特之处在于，我们将把这种方法应用于五个不同的全球模型，以提供对模型响应的可靠评估，并首次确定模型差异的原因。我们的可行性研究成功地证明了这种方法的有效性和价值。使用大气成分测量，我们将开发新的多变量观测约束，使我们能够减少在关键过程中的不确定性，通过应用广义Likestival不确定性估计方法在这一领域的第一次使用这些约束，我们将量化的贡献，从变化的排放量和气候变化的O3和CH 4自前工业时代。这将首次明确O3和CH 4辐射强迫变化的来源归属，为气专委今后的评估提供信息。我们将确定与观察到的趋势相匹配所需的因素，使我们能够解释为什么当前的模型无法重现观察结果。我们将采用相同的技术来传播我们对过程和排放的理解中的不确定性，以提供给定排放途径的预计未来O3和CH 4的正式不确定性。这种新的分析方法是及时的，大大受益于我们参与国际化学气候模型倡议（CCMI）过去和未来大气成分变化的多模型评估，使我们能够解释模型结果的多样性，并减少由此产生的大气变化预测的不确定性。