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Research

We use numerical models, coupled with satellite, balloon, aircraft and ground-based observations, to improve our understanding of atmospheric chemistry and transport. We develop novel, world-leading three-dimensional models of atmospheric chemistry which underpin much of our research and widespread international collaborations.

Our research is summarised in six categories below. The bullets link to specific topics that are summarised on our main TOMCAT research pages.

Stratospheric Ozone Layer Depletion and Recovery

We study the depletion and recovery of the stratospheric ozone layer. As well as researching the fundamental processes which control stratospheric ozone, we use our models to inform policy makers of the impact of actions taken under the Montreal Protocol (see figure).

Chemistry-Climate Interactions

The two-way coupling between changes in climate and composition are important for many reasons. Changes in atmospheric composition will affect climate, and changes in climate will also feedback on, for example, atmospheric chemistry. Much of our work also relates to ozone chemistry-climate interactions in the stratosphere.

Greenhouse Gas Emissions and Budgets

We use inverse model and data assimilation to estimate surface emissions of important greenhouse gases (GHGs) including CH4, CO2 and N2O.

Air Quality and Tropospheric Composition

We use state-of-the art satellite observations of chemistry and aerosols in the lowest few km of the atmosphere to study air quality. The satellites give a large-scale view of air quality (see image) which complements observations made at the surface.

Upper Atmosphere

Metal layers layers in the mesosphere and lower thermosphere (MLT) are produced by ablating meteors. We use them as tracers to test model chemistry and transport in this region. The image shows ACE-FTS satellite observations of enhanced N2O in the MLT and our calculations with a new parameterisation for this source in a climate model.

Numerical Model Development

We develop and use global models of atmospheric chemistry and dynamics. A major part of this is TOMCAT and related models (e.g. SLIMCAT, INVICAT), but we also contribute to the development of coupled chemistry-climate models such as UKESM and WACCM. We are now using Machine Learning to remove model bias and produce optimised gap-free satellite-based datasets.