How do we find signals in data? In the era of ‘big data’ we are often faced with finding subtle signals obscured by noise and natural variability.
In this course we will learn to use statistical techniques to reveal these signals – importantly, we will learn to do this in an objective way to make sure our results are robust and stand the test of time.
Techniques covered in class include: basic statistics, regression, principal component/empirical orthogonal functions, and machine learning topics including neural networks, random forest, Gaussian process, self organizing maps, and convolutional neural networks. In-class analysis will span examples across disciplines.
Taught Fall 2020, Spring 2021, Spring 2023
We will explore different parts of the climate system and their interactions.
We will examine interactions between different components of the climate system and Radiative, dynamic, thermodynamic, chemical, and feedback processes affecting the climate system. Natural and anthropogenic drivers of climate change will be discussed along with past and present climate variability and sensitivity.
A focus will be put on global simulations including the structure of climate models, their components, parameterizations, and attributes; and current climate modeling results and predictions of future climate.
Taught Fall 2021