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 addressed 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 applied 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 have applied these approaches to a number of independent global models to provide a robust assessment of model responses and to identify the causes of model differences for the first time.
The overarching aim of the project was to provide fresh scientific insight into the chemical and dynamical processes governing tropospheric OH and the related gases ozone and methane. This allowed us to quantify the importance of different processes, explain the diversity in model assessments of past and future atmospheric composition change, and attribute observed changes to specific drivers.
Our principal scientific objectives were:
1. To identify the main causes of uncertainty in modelled tropospheric OH, O3, and CH4, allowing us to quantify for the first time how our understanding of different processes and variables contributes to variation in atmospheric composition, and to explain the large differences in model responses seen in assessments of past and future atmospheric composition
2. To use atmospheric composition observations to place formal statistical constraints on model uncertainty, allowing us to determine which processes can be efficiently tested with observations and permitting design of more critical, process based approaches to model evaluation
3. To apply these novel approaches and constraints to provide a new and more robust attribution of the causes of past and future O3 and CH4 changes, and permit formal quantification of the associated uncertainty.
Duration of project: Jan 2016 - Dec 2018
NERC Reference: NE/N003411/1