Cloudy with a chance it rained

Supervisor: Dr Tamara Fletcher

Department: School of Earth and Environment, University of Leeds


Cloud is the greatest source of uncertainty in climate models. Cloud model performance is tested against recent observations, yet the predicted future climate is outside this range of data. Testing model performance in a past analogue for future climate (the Pliocene) is one solution; however, there are currently no methods for estimating cloud in the distant past. Dr Tamara Fletcher’s current research at Leeds is looking for ways to reconstruct cloud in the past using evidence from plants.

In this Research Experience Placement, we will be conducting foundational work to guide the analysis of leaf-trait data from light-mediated growth experiments. We will test whether state-of-the-art automated leaf feature detection and measurement techniques are sufficiently precise and accurate compared to trained researchers and/or citizen scientists.
This will include the following:
1. Local fieldwork collecting silver birch leaves and recording field data.
2. Macromorphological measurement of the leaves.
3. Preparing leaf impressions.
4. Mounting impressions on microscope slides.
5. Capturing microscope images of leaf regions of interest.
6. Preparing and uploading images to the online citizen science platform, ‘Zooniverse’
7. Engaging with participating citizen scientists.
8. Measuring micromorphological features.
9. Annotating microscope images.
10. Running our images through recently published computer vision/machine learning models for automated feature detection and measurement.

Training in all tasks will be provided. Some experience working with microscopes and coding in python is preferred but not a requirement. The student will also have the opportunity for authorship by contributing to a peer-reviewed article anticipated from this work.

The student will gain experience in:
• Specimen preparation
• Microscopy
• Quantitative morphology
• Citizen Science engagement
• Scientific writing
• Implementing machine learning models
• Field work

For more information about Dr Fletcher’s research see