Instructor: Prof. Marek Stastna
Email: mmstastna@uwaterloo.ca

Teaching Assistant: Sierra Legare (will deliver several focused lectures)

Course Dates: January 8th - April 3rd, 2024
Mid-Semester Break: February 19th - 23rd, 2024
Lecture Times: Mondays & Wednesdays | 10:30 AM - 12:00 PM

Registration Fee: PSU Students - Free | Other Students - CAD$500

Capacity Limit: 35 students

Format: Hybrid synchronous delivery

• In-Person: this course will be co-taught with a Topics Course (AMATH 900) in Applied Mathematics at the University of Waterloo
• Online: via Zoom

Course Description

This course will provide a graduate level introduction to the mathematical tools used to construct and analyze models of the climate system in the context of climate change. Course material will span aspects of the climate system, with about 60% focused on classical topics such as energy balance models, climate time series, and the modelling of ENSO. The remaining 40% of the course will focus on emerging topics such as climate change and economics, and novel data science/machine learning methods. The course will be taught from a constructivist point of view, meaning that simple models will be developed via ad hoc mathematical modelling, as opposed to systematic reductions (e.g. perturbation theory, Galerkin projection) of more complex models. There are no formal prerequisites, except for mathematical maturity.  However, students will be expected to use codes that are provided in Matlab, and make simple extensions to these codes. Students will be expected to do more expository writing than a typical applied mathematics course (and much more than a pure mathematics course).

Attendance: Essential. A big part of this topic is interacting with peers.

Evaluation:

1. 3 substantial assignments with the first one mostly graded for completeness so we can establish expectations. 10+25+25=60%
2. Reaction piece to a climate related e-talk selected from a provided list of available seminars. Note I will explicitly ask “What did ChatGPT answer?” And will compare the ChatGPT response to what is handed in. 10%
3. Project talk: short “why should your peers be interested in this topic talk” 10%
4. Project write up: Projects will be chosen from a curated list with two check in sessions during the term. 20%