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

了解对流层中羟基（OH）自由基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。在过去的一个世纪里，由于人类活动，观测到的大气中臭氧和甲烷的丰度大大增加，这些气体的命运通过短命的OH自由基紧密耦合。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观测到的大气中甲烷的积累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，或在过去二十年中观测到的最近的臭氧趋势。本项目通过采用新颖的统计方法来量化全球模式中OH、O3和CH4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，从而解决了这些弱点。我们将采用经过验证的模拟方法来重现计算成本高昂的大气模型的响应，并允许对过程对微量气体丰度不确定性的贡献进行更完整和定量的评估。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。我们的可行性研究已经成功地证明了这种方法的有效性和价值。利用大气成分测量，我们将开发新的多变量观测约束，使我们能够通过首次在该领域应用广义似然不确定性估计方法来减少关键过程中的不确定性。利用这些限制条件，我们将量化自前工业化时代以来排放和气候变化对臭氧和甲烷变化的贡献。这将使O3和CH4辐射强迫变化的首次明确来源归因成为可能，为IPCC未来的评估提供信息。我们将确定与观测到的趋势相匹配所需的因素，使我们能够解释为什么目前的模型不能再现观测结果。我们将采用同样的技术来传播我们对过程和排放的理解中的不确定性，以提供对给定排放途径预测的未来O3和CH4的正式不确定性。这种新的分析方法是及时的，并且从我们参与国际化学-气候模式倡议（CCMI）对过去和未来大气成分变化的多模式评估中受益匪浅，使我们能够解释模式结果的多样性并减少大气变化预测的不确定性。

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.