CLIMATE MODELLING

The course is one of the core educational activities of the Master's Degree Program in Environmental Sciences ( Curriculum in Global Environmental Change). It is the natural complement of the course DATA ANALYSIS and aims at providing students with an introduction to statistical analysis of climate data. Important statistical methods including regression modeling, auto-correlation, smoothing and spectral analysis will be discussed and used to enhance data interpretation and answer scientific questions. Practicals with the R statistical software will be an integral part of the course.

Describe what a self‐describing gridded binary format is and identify major formats currently in use in climate analysis
Know how to gain information about such a dataset when presented with one.
Delve into a dataset to reduce and prepare it for further analysis.
Perform a variety of standard types of data analysis (e.g. correlations/regressions/EOFs).
Present project results in a clear and concise manner.

A basic understanding of Statistics (see for instance David S. Moore (2010) The Basic Practice of Statistics W. H. Freeman and Company). Programming of some form will be helpful.

Data storage methods (types of data formats and how best to deal with them)
Inference for climate data
Regression models
Time series components
Exploratory methods for time series
Spectral analysis
Statistical models for climate time series

K. McGuffie, K. and Henderson-Sellers, A.. (2014) A Climate Modelling Primer, Fourth edition, John Wiley & Sons
Chandler, R. and Scott, M. (2011). Statistical Methods for Trend Detection and Analysis in the Environmental Sciences. Wiley
von Storch, H. and Zwiers, F.W. (1999). Statistical Analysis in Climate Research. Cambridge University Press

Final examination will consist of two steps:
1) preparation of an individual report regarding the analysis of an dataset.
The class project will entail choosing a problem (mutually agreed upon), writing code to solve it, and write a report which provides the background, motivation, solution method used and results. This project should ideally be a task you need to do for your thesis so that it is serves multiple purposes.
2) oral illustration of the report.

Grades will be determined by an oral exam (50%) and by a project (50%),

This course is based on lectures, which will cover the major topics, emphasizing and discussing the important points.
Theoretical lectures will be complemented by exercise classes and lab sessions. The statistical software used in the course is R (www.r-project.org).
The personal participation is important, and it is will help the student to learn more efficiently to read the assigned material to reinforce the lectures.
R scripts from various sources may be used to reinforce the material.

English

The attendance to each class (listening) and a genuine effort in doing the suggested exercises are two of the most important factors affecting the success in this course. Statistical analysis require both listening and doing.

oral

This subject deals with topics related to the macro-area "Climate change and energy" and contributes to the achievement of one or more goals of U. N. Agenda for Sustainable Development