STATISTICAL METHODS FOR CLIMATE CHANGE ANALYSIS

Statistics provides a powerful approach to make sense of data and to take into account the uncertainties which come from the randomness of complex systems. The course presents some statistical tools to analyse weather/climate data, starting from the initial concepts of randomness and probability moving into more complex data analysis approaches which can accurately describe the data generating processes.

Students will be able to correctly carry out a statistical analysis of climate-related variables using a statistical software, identifying the most suitable statistical approach for the problem under study and identifying potential benefits and pitfall of various analytical approaches.

No formal pre-requisite. The course will make use of some mathematics concepts such as functions, integrals, derivatives and matrices. Moreover the course will assume a undergraduate level understanding of some probability and statistics concepts such as probability distributions, descriptive statistics, estimation and statistical hypothesis testing. It is recommended that student review these topics before the course begins if necessary.

The course deals with analysing the statistics of weather and climate by taking uncertainties and random fluctuations into account and provide objective assessment of various statistical hypotheses. The course also deals with analysing weather/climate fields to construct main patterns or modes of variability that control weather/climate variation. The course discusses basic concepts of probability and statistics such as stationary time series, statistical significance and hypotheses testing, spectral analysis, regression analysis, empirical orthogonal functions and their extension, the analysis of extreme values. Students will also be encouraged to identify statistical approaches used in scientific literature and to evaluate their suitability and use in the class.

Lecture notes and additional external material identified by the instructor. Furthermore, this text might prove a useful reference Statistical Analysis in Climate Research by von Storch and Zwiers, Cambridge Univ Press.

1) Class participation and homeworks 30%
Students are expected to play an active role during the course and the labs and to hand in a periodic assessed homework.

2) Project 40%
Each student will be assigned a data set about a climate-change problem to analyse (students are encouraged to suggest a dataset they are wish to analyse). The outcome of the analysis needs to be detailed in a short report.

3) Final exam 30%
The final exam consists of an oral discussion of the project.

Theoretical lectures complemented by lab classes. The statistical software used in the course is R (www.r-project.org).

English

oral