Climate Modelling - Detailseite

This course provides a comprehensive introduction to the theoretical foundations and practical implementation of climate models used to study the Earth’s climate system. Students will begin with an overview of the climate system and the hierarchy of climate models, followed by simplified approaches such as energy balance models (EBMs) and basic numerical methods for solving ordinary differential equations. The course then progresses to the core components and physical processes represented in General Circulation Models (GCMs), including the numerical formulation of atmospheric and oceanic equations. Students will gain hands-on experience in constructing climate models and learn techniques for climate downscaling. The course concludes with the compilation and execution of a state-of-the-art Regional Climate Model (RegCM5), providing practical skills for regional climate simulations and analysis.

This is an introductory course in Bayesian statistical modelling. We will read chapters of the textbook by McElreath (2020, 2^nd edition), discuss the content and apply the methods in exercises using the brms package in R.

Learning objectives

Students ...

… have experienced and understood the fundamental philosophy behind Bayesian probability theory,

… have acquired the skills to do Bayesian analysis using the brms package in R,

… know which resources to consult for further study.

Topics

• Philosophical difference between classical and Bayesian statistics
• The R package ‘brms’
• Numerics: grid approximation; quadratic approximation; Markov Chain Monte Carlo
• Working with samples from posterior; posterior predictive checks; prior predictive simulation
• Linear regression
• Categorical predictors; interactions
• Confounding effects; model comparison; regularizing priors
• Generalised Linear Models: Binomial regression; Poisson regression; over-dispersion; zero-inflation
• Hierarchical models: Varying intercepts; varying slopes; multi-level posterior prediction choices

Format

The mode of working is a mix of independent textbook study; collective discussion; independent and collective problem solving; homework; and lecture-style inputs from the teacher as needed.

The open-source software STAN will be used via the brms package R. An introduction to and help with brms/STAN will be provided. Students need a good working knowledge of R!

Homework will be submitted using R Markdown.

Allocation of places

Due to the mode of working in this course places are limited. Students are required to register via Agnes. Priority will be given to 4^th semester students of the Global Change Geography Master.

McElreath. 2020 (2^nd edition). Statistical Rethinking: A Bayesian Course with Examples in R and Stan. CRC Press

Towards the end of the semester the students select an individual exam project involving data analysis using brms, which has to be submitted via R Markdown, just like an extended homework.

A firm background in classical statistics and the software R is required, equivalent to a full grasp of “Quantitative Methods for Geographers”.