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

点击翻译按钮获取中文摘要

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.