Schedule

Module 1: Probabilistic climate attribution

Fredi Otto, Imperial College London, London, UK

Clair Barnes, Imperial College London, London, UK

Attribution science is a relatively recent branch of climate science, evaluating the extent to which anthropogenic climate change has altered the likelihood and intensity of extreme weather events around the world. This short course provides participants with the knowledge and tools to conduct attribution analysis for extreme weather events, with a particular focus on heatwave events. By the end of the course, students will have the basic skills to conduct their own attribution analyses and calculate factual and counterfactual temperatures to be used in mortality attribution. Topics covered will include:

• Methods in attribution science
• Visualising weather and climate data
• Defining a meteorological event
• Software tools for probabilistic attribution
• Interpreting attribution results

The training includes lectures and hands-on computer labs using real data, with time to discuss your own research questions.

Module 2: Bayesian models for climate and environmental health

Lecturers

Garyfallos Konstantinoudis, Imperial College London, London, UK

Robbie Parks, Columbia University, New York, US

Course description

In environmental and climate epidemiology, spatial and spatiotemporal methods are increasingly used with high-resolution environmental and health data, which allows detailed insights into associations between exposures and outcomes. In this context, Bayesian methods provide a natural setting to incorporate uncertainty and prior knowledge, borrow information between neighbouring units, and create hierarchical structures.  However, appropriate usage of these methods requires careful consideration and knowledge of building technical models, which can be intimidating to the uninitiated user. This course provides a practical and approachable introduction to:

• The basics of Bayesian inference
• Hierarchical models
• How to choose priors
• Working with different types of data (spatial, temporal, continuous, categorical)
• Software tools for Bayesian analysis
• Real-world examples and how to apply these methods to your own research
• Bayesian distributed lag non-linear models

The training includes lectures and hands-on computer labs using real data, with time to discuss your own research questions.