Group research

Our core research goal is to understand which processes are responsible for observed variations of Earth’s atmospheric chemical composition.

This is important for two reasons: 1) atmospheric composition directly controls the planet's radiative balance, determining the degree of surface warming and climate change and 2) it defines the oxidation capacity of the global troposphere, which dictates the quality of the air we live and breathe.

To achieve our goal, we develop advanced computational models to simulate these complex chemical and physical interactions. We also develop innovative Bayesian inverse methods and AI approaches to link these models to Earth Observation and other atmospheric data to extract quantitative information about the underlying processes, constrain uncertainties in our predictions, and provide a comprehensive, data-driven understanding of Earth's evolving atmosphere.

The innovative methods and models we have pioneered for studying Earth’s atmosphere provide a powerful, transferable framework that we are now using to study planetary atmospheres across our solar system and beyond.

Earth

Our biggest focus is on our home planet.

Here are some research questions that are currently keeping us busy:

Which natural and human-driven processes are responsible for observed variations in atmospheric CO2 and methane?
How can we infer combustion emissions of CO2 from atmospheric measurements of CO2?
Which processes are driving observed changes in tropospheric chemistry and how do they affect surface air quality?

To answer these questions, most of our research is associated with computational projects, but we also design and run field campaigns and develop instrument technology.

Computational Projects

We develop novel mathematical models to reconcile data with current knowledge. Often these models are too complex to resolve analytically so we tend to use computational approaches.

We have also developed Bayesian inverse methods to to interpret atmospheric data.

A growing part of our work uses AI methods for data reduction and to aid scientific interpretation.

Earth Observation Data

We have played an ongoing role in developing Earth Observation mission concepts and data products.

A prominent example is that we pioneered the use of satellite data and atmospheric chemistry models to understand tropospheric chemistry, most notably generating the first global emission maps of isoprene. This research is crucial for observing how vegetation responds to climate change and developing better ozone mitigation strategies.

Currently, we are the UK lead of the French-UK MicroCarb mission.

Field Campaigns

To help answer our science questions, we have led, co-led, and supported a range of ground-based and aircraft campaigns, e.g. GAUGE, BORTAS, CAST.

Our current projects are GEMINI-UK (focused on GHG budgets over the UK), GEMINI-Edinburgh (focused on GHG budgets and particulate matter levels for the City of Edinburgh), and CurFEW that is focused on improving our knowledge of the processes that determine wetland emissions over Africa and how they might change in a warmer climate.

Technology Development

We have a heritage of developing instruments, typically working with colleagues at the Astronomy Technology Centre in Edinburgh.

An example project is GHOST that flew on the NASA Global Hawk over the eastern Pacific in 2015.

Our two current projects, both led by Jerome Woodwark, are NIMCAM and SNEEZI.

Mars

We are advancing an established 3-D Mars Global Circulation Model (MGCM), continuously refining its chemical core—including crucial organic and chlorine reaction pathways—to accurately interpret the high-resolution data from the NOMAD and ACS instruments on the ExoMars Trace Gas Orbiter (TGO). This effort significantly expands upon our initial 1-D modelling framework.

We are driven by two broad research questions:

Chemical Landscape: How dynamic and extensive is the theoretically supported atmospheric chemistry on Mars, and what are the major seasonal drivers of change?
The Model-Data Loop: How can cutting-edge 3-D modelling be directly used to guide the development of exploratory retrieval algorithms, ensuring we maximize the scientific yield from TGO's observed spectra? Can new satellite observations reveal atmospheric chemistry that holds the key to explaining the puzzling observations of Martian methane?

Exoplanets

Through extensive collaborations, we are pioneering the study of exoplanetary atmospheric composition. Our approach leverages advanced models and cutting-edge data—particularly from the James Webb Space Telescope (JWST)—to tackle fundamental questions that define our place in the cosmos:

Atmospheric Sustainability: What exotic chemical systems can persist in alien atmospheres, and how does JWST data change our understanding of them?
Planetary Dynamics: How do the extreme, tidally locked environments of distant worlds shape their atmospheric circulation and the distribution of atmospheric composition, and how do these regimes fundamentally differ from Earth’s?
The Origins of Life: Can we identify an exoplanet analogue for early Earth that possesses the right conditions to foster the development of pre-biotic molecules?
Data Science Frontier: How can we effectively harness machine learning to rapidly process and extract profound discoveries from the massive data streams provided by JWST?

In a recent commentary, we argue for tighter links between experimental, modelling, and observational communities to further our interpretation of spectrally-resolved exoplanet data.

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