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

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.

The wider beneficiaries of this work include atmospheric scientists, statistical researchers, policy makers and the general public.

Atmospheric scientists will benefit from new quantitative insight into the processes and variables governing atmospheric composition and its changes. While advancing scientific understanding, the project will inform model development and promises more confident prediction of future atmospheric composition change. Development of the Hadley Centre's UKCA model will benefit current and future users of this national model and contribute to UK Earth System Model (UK-ESM) development. Our involvement in the international CCMI activity ensures that our results and methods will reach a global audience in the research community in a timely manner, and provides a pathway to inform IPCC climate, WMO ozone and HTAP pollutant transport assessment reports. The emulators developed in the project will be of value themselves as "toy" models allowing emission and process-based scenarios to be explored without the need for computationally demanding model simulations.

Statistical researchers will benefit from new interest in uncertainty quantification from the atmospheric chemistry and composition community, presenting new challenges and encouraging development of new approaches that will have lasting benefits across the disciplines.

The international policy community will benefit from improved and more confident assessment of atmospheric composition change through IPCC, WMO and HTAP assessments and from the improved capability of modelling tools such as the UKCA and GISS CCMs which are used for these assessments. Reduced uncertainty in the atmospheric response to emission changes provides a more solid foundation for climate policy, and improved understanding of the processes driving background ozone permits more focussed air quality policies addressing this air pollutant.

The wider public has a general interest in global change and air quality, and will benefit from our improved capability to represent atmospheric composition indirectly through more confident policy-making that is based on reduced uncertainty in atmospheric responses.

We will engage with the research community through participation in national and international conferences and CCMI workshops, and through publication of our results in the peer-reviewed literature. We will engage with the general public through a project website, blogs and interaction with the media, and will contribute to schools outreach activities through a focus on atmospheric composition and environmental uncertainty. We are already involved in IPCC, WMO and HTAP assessments, and will ensure that key outcomes of the project reach governmental and policy communities through these fora.

We will organise a 2-day workshop in Lancaster in the final months of the project that focusses on uncertainty in atmospheric composition and brings together the atmospheric composition and statistics communities with representatives from the policy-making arena.