Project Description:
Aerosols play a major role in climate, but they remain one of the largest sources of uncertainty in climate models. One way we study this uncertainty is
through a perturbed parameter ensemble (PPE), where we run the same model many times with different values for uncertain aerosol processes such as
emissions, chemistry and deposition. Doing so creates a range of plausible atmospheres and helps us identify which processes drive model uncertainty and
how well the model matches real world observations.
Until now, our research has focused on monthly averages, which smooth out the day to day variability of aerosols. Daily data can look very different because
aerosols are short lived and influenced by weather. In this project, you will analyse the daily dataset from a new PPE, giving our first detailed view of day to
day variability. You will assess how much conditions change from one day to the next, how well the model captures daily observations, and whether the key
uncertain processes differ at finer timescales. You will also test whether grouping days into categories such as wet and dry, or high and low aerosol days,
reveals patterns hidden by monthly averages.
A student who enjoys data analysis and problem-solving will be a great fit. You will gain hands on experience with climate data, present your results to our
research group and an external team, and have the opportunity to attend the UKCA Science Meeting to hear about current modelling priorities and ongoing
challenges in the model you will be working with.
The project will follow a structured six week plan:
- Week 1: Set up on the HPC, get familiar with the ensemble, and explore daily vs monthly behaviour.
- Week 2: Bring in daily observations and compare model–observation distributions.
- Week 3: Run daily scale sensitivity analyses to see whether the dominant uncertain parameters change from day to day.
- Weeks 4–5: Test alternative ways of grouping the daily data, for example by meteorological conditions (such as wet versus dry days) or by aerosol state
(such as high AOD versus low AOD days) and examine how model biases and parameter sensitivities differ between these groups. - Week 6: Summarise what daily data reveals that monthly averages hide and clearly demonstrate how daily variability and grouped day analysis change our
understanding of the PPE compared to the existing monthly mean perspective.
Interactions: The supervisor will check in with the student daily to discuss goals and progress, provide feedback on figures and interpretation, and guide next
steps. The student will be fully embedded in an active research environment: they will take part in weekly Aerosol, Clouds & Climate group meetings, which
combine skill‑building sessions with research talks, and join our smaller PPE subgroup to discuss current modelling challenges and uncertainty. They will
present their findings to the external project “Towards maximum feasible reduction in aerosol radiative forcing,” gaining experience in communicating results
to collaborators beyond our group. The student will also attend the UKCA Science Meeting to hear about modelling priorities relevant to their work.
Skills gained: The student will gain an understanding of how climate models work and where uncertainty comes from, develop familiarity with aerosol
processes and how parameter choices influence model output, and build experience handling large datasets on an HPC. They will also strengthen their data
analysis and coding skills and learn to communicate their findings clearly to an audience of researchers.
Pre-requisites:
N/A
Supervisory Team:
Lea Prevost
Contact:
Lea Prevost: [email protected]