New course: Confronting the Climate Crisis

12. January 2024 · 4 comments · Categories: courses, Uncategorized

It’s the first week of term here in Toronto, and I’m busy launching a new course. It’s a small-seminar, first-year undergraduate course, open to any student in the Faculty of Arts and Science, called Confronting the Climate Crisis. I’m launching it as a small seminar course this year as a pilot project, with the aim that we go big next year, turning it into a much larger lecture course, open to hundreds (and maybe thousands) of students. I’ll have to think about how to make it scale.

The idea for this course arose in response to an initiative at the University of Barcelona to create a mandatory course on the climate crisis for all undergraduate students, to meet one of the demands of a large-scale student protest in the fall of 2022. The University of Barcelona expects to launch such a course later this year. Increasingly, our students are demanding that Universities respond to declarations of a climate emergency (e.g. by the Federal Government and the City of Toronto) by re-thinking how our programs are preparing them with the resilience and skills needed in a world that will be radically re-shaped by climate change in the coming decades.

The design of this course responds directly to the challenge posed at U Barcelona: If every student were required to take (at least!) one course on the climate crisis, what would such a course look like? Climate change is a complex, trans-disciplinary problem, and needs to be viewed through multiple lenses to create an integrated understanding of how we arrived at this moment in history, and what paths we now face as a society to stabilize the climate system and create a just transition to a sustainable society. The course needs to give the students a clear-eyed understanding of how serious and urgent the crisis is, but also needs to give them the tools to deal with that understanding, psychologically, politically, and sociologically. So it needs to balance the big picture view, and a very personal response: what do you do to avoid falling into despair, once you understand.

It’s not clear to me that in any reasonable amount of time we could get the University of Toronto to agree to make such a course mandatory for every student, given the complex and devolved governance structure of the second largest university in North America. But we can make a lot of progress by starting bottom up: by launching the course now, we intend to provoke a much wider response across the University: How are we preparing all of our students for a climate changed world? What do different departments and programs need to do in response? If other departments want to add this course to their undergrad programs, I’ll be delighted. Or if they want to create versions of the course that are more specifically tailored to their own students’ needs, I’ll be equally delighted.

Alright, enough pre-amble. Here’s the syllabus entry:

“This course is a comprehensive, interdisciplinary introduction to the climate crisis, suitable for any undergraduate student at U of T. The course examines the climate crisis from scientific, social, economic, political, and cultural perspectives, from the physical science basis through to the choices we now face to stabilize the climate system. The course uses a mixture of lectures, hands-on activities, group projects, online discussion, and guest speakers to give students a deeper understanding of climate change as a complex, interconnected set of problems, while equipping them with a framework to evaluate the choices we face as a society, and to cultivate a culture of hope in the face of a challenging future.“

And here’s the outline I’ve developed for a 12-week course:

  1. How long have we known?
    • Course intro and brief overview of the history of climate science
  2. What causes climate change?
    • Greenhouse gases – where they come from and what they do
    • Sources of data about climate change
    • How scientists use models to assess climate sensitivity
  3. How bad is it?
    • Future projections of climate change
    • Understanding targets: 350ppm, 1.5°C & 2°C; Net Zero
    • Irreversibility, overshoot, long-term implications, and emergency measures (geoengineering)
  4. Who does it affect?
    • Key impacts: extreme weather, sea level rise, ocean acidification, ecosystem collapse, etc
    • Regional disparities in climate impacts and adaptation, and the rise of climate migrants
    • Inequities in responsibility and impacts – the role of climate justice.
  5. Do we have the technology to fix it?
    • Decarbonization pathways
    • Sectoral analysis: energy, buildings, transport, food systems, waste, etc
    • Interaction effects among climate solutions
  6. Can we agree to fix it?
    • International policymaking: UNFCC, IPCC, Kyoto, Paris, etc.
    • Policy tools: carbon taxes, carbon trading, subsidies, direct investment, etc.
    • Barriers to political action
  7. What will it cost to fix it?
    • Intro to climate economics
    • Costs and benefits of adaptation and mitigation
    • Ecomodernism vs. Degrowth
  8. What’s stopping us?
    • Climate communication and climate disinformation
    • The role of political lobbying
    • How we talk about climate change and the role of framing
  9. What are we afraid of?
    • The psychology of climate change
    • Affective responses to climate change: ecoanxiety, doomerism, denial, etc.
    • Maintaining mental health in the climate crisis
  10. How can we make our voices heard?
    • Protest movements and climate activism
    • Theories of Change
    • Modes of activism and the ethics of disruptive protest
  11. What gives us hope?
    • Constructive hope as a response to eco-anxiety
    • The role of worldviews, culture, and language
    • Reconnecting with nature
  12. Where do we go from here?
    • Importance of systems thinking and multisolving.
    • The role of storytelling in creating a narrative of hope
    • Making your studies count: the role of universities in a climate emergency.

Course Policy for use of generative AI

06. January 2024 · Write a comment · Categories: courses · Tags: ,

I’m teaching a new course this term, called Confronting the Climate Crisis. As it’s the first time I’ve taught since the emergence of the latest wave of AI chatbots. Here’s what I came up with:

The assignments on this course have been carefully designed to give you meaningful experiences that build your knowledge and skills, and I hope you will engage with them in that spirit. If you decide to use any AI tools, you *must* include a note explaining what tools you used and how you used them, and include a reflection on how they have affected your learning process. Without such a note, use of AI tools will be treated as an academic offence, with the same penalties as if you had asked someone else (rather than a bot) to do the work for you.

Rationale for this policy: In the last couple of years, so-called Artificial Intelligence (AI) tools have become commonplace, particularly tools that use generative AI to create text and images. The underlying technology uses complex statistical models of typical sequences of words (and elements of images), which can instantly create very plausible responses to a variety of prompts. However, these tools have no understanding of the meanings that we humans attach to words and images, and no experience of the world in which those meanings reside. The result is that they are expert at mimicking how humans express themselves, but they are often factually wrong, and their outputs reflect the biases (racial, gender, socio-economic, geographic) that are inherent in the data on which the models were trained. If you choose to use AI tools to help you create your assignments for this course, you will still be responsible for any inaccuracies and biases in the generated content.

More importantly, these AI tools raise important questions about the nature of learning in higher education. Unfortunately, we have built a higher education system that places far too much emphasis on deadlines and grades, rather than on learning and reflection. In short, we have built a system that encourages students to cheat. The AI industry promotes its products as helpful tools, perhaps no different from using a calculator in math, or a word processor when writing. And there are senses in which this is true – for example if you suffer from writer’s block, an AI tool can quickly generate an outline or a first draft to get you started. But the crucial factor in deciding when and how to use such tools is a question of what, exactly, you are offloading onto the machine. If a tool helps you overcome some of the tedious, low-level steps so that you can move on faster to the important learning experiences, that’s great! If on the other hand, the tool does all the work for you, so you never have to think or reflect on the course material, you will gain very little from this course other than (perhaps) a good grade. In that sense, most of the ways you might use an AI tool in your coursework are no different from other forms of ‘cheating’: they provide a shortcut to a good grade, by skipping the learning process you would experience if you did the work yourself.

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This course policy is licensed under a Creative Commons Licence CC BY-NC-SA 4.0. Feel free to use and adapt for non-commercial purposes, as long as you credit me, and share alike any adaptations you make.

Outline for my new undergrad course

06. January 2011 · 17 comments · Categories: courses

I mentioned before that I’m teaching a new course this term, a first-year undergraduate seminar course on climate computing. Course starts next week, so I’m busy putting together some material. It’s intended to be pretty open ended, as it’s a small group seminar, and we can jump into topics that the students are interested in. So I put together a core set of topics I want to cover, and then a long list of other possible topics. Here’s what I have so far:

Core topics:

Part 1: Background & History

Week 1: Climate Science BC (Before Computers), in which we cover Fourier, Tyndall, Arrhenius, Callendar, and the discovery of global warming. We’ll introduce the key concepts for understanding how the physical climate system works.

Week 2: Taming Chaos in which we talk about Bjerknes and Lorenz, and look at the basic equations for modeling the atmosphere, and a gentle introduction to Chaos theory.

Week 3: The Giant Brain in which we talk about von Neumann, Charney, ENIAC and the first general circulation models.

Part 2: Basics of Climate Modeling

Week 4: Inside the simulation (also known as “Computational Fluid Dynamics on a Rotating Sphere for beginners”), where we look at the major elements of a climate model, including grids, dynamics, radiation, parameterizations, etc.

Week 5: What happens at the Boundaries? in which we look at the physical boundaries (land and ocean, the boundary layer), spatial boundaries (subgrid processes), temporal boundaries (start states, long runs, etc), and climate state boundaries (forcings, emissions scenarios, etc), and talk about the difference between Initial Value and Boundary Value problems.

Week 6: Towards Earth System Models in which we look at the growing complexity of modern GCMs, and the trade-offs between more resolution, more earth system processes, and more complexity.

Part 3: Choosing and Using Models

Week 8: On the Catwalk in which we look at the vast range of different types of models that are available, and what they are used for.

Week 9: Experimentation in which we look at what’s involved in running a model, and the kinds of experiments you might do with one. If we’re lucky, we’ll get to try running some experiments for real.

Week 10: How good are the models? in which we look at how to test and validate models, what it means to “do science” with a model, what model inter-comparison projects tell us, and some of weaknesses of current earth system models.

Part 4: What we can know, and what we can do

Week 11: Knowledge and Uncertainty, in which we talk about what we know and what we don’t know about climate change, sources of uncertainty, and whether we can predict the future. We’ll also explore how climate models interact with other types of knowledge about global climate change.

Week 12: Decisions, Decisions, Decisions in which we look at what policymakers need, and what they get. We’ll talk about the IPCC process, and maybe a bit about some of the policy options. We’ll talk about the need for better predictions of climate extremes, and regional impacts. And we’ll look at the difference between GCMs and IAMs.

Week 13: Enough talk, time for action! in which we face up to the question that given we now have to learn how to manage the earth’s climate systems, what should we be doing about climate change, and what other tools do we need in order to be successful?

Additional Topics:

We can include any of these based on interest and enthusiasm (but probably not all of them!). Some of these stray away from the “computing” them of the course, so we might need to agree on some criteria for which ones to include. In no particular order:

  • Carbon calculators (and other softwre for personal/community decision support);
  • Data Collection for weather and climate: how the global observing system works;
  • Supercomputers and the path to exascale computing;
  • Media potrayals of climate models and their projections;
  • The role of Free/Open source software in climate science;
  • The carbon footprint of computing;
  • Other sources of evidence about climate change: paleoclimate, observational data, measurable impacts;
  • Controversy and disinformation – how do you know who to trust? Is there really a debate, and if so, what about?
  • How bad will it get?
  • Climate Change and other global issues: over-population, peak oil, conservation of ecosystems, international conflict, food security, renewable energy, etc.;
  • Geo-engineering;
  • Climate Ethics: inter-nation and inter-generational issues.

New undergrad course on climate models

04. November 2010 · 1 comment · Categories: climate modeling, courses

After an exciting sabbatical year spent visiting a number of climate modeling centres, I’ll be back to teaching in January. I’ll be introducing two brand new courses, both related to climate modeling. I already blogged about my new grad course on “Climate Change Informatics”, which will cover many current research issues to do with software and data in climate science.

But I didn’t yet mention my new undergrad course. I’ll be teaching a 199 course in January, which I’ve never done before. 199 courses are first-year seminar courses, open to all new students across the faculty of arts and science, intended to encourage critical thinking, communication and research skills. They are run as small group seminar courses (enrolment is capped at 24 students). I’ve never taught one of these courses before, so I’ve no idea what to expect – I’m hoping for an interesting mix of students with different backgrounds, so we can spend some time attacking the theme of the course from different perspectives. Here’s my course description:

“Climate Change: Software, Science and Society”

This course will examine the role of computers and software in understanding climate change. We will explore the use of computer models to build simulations of the global climate, including a historical view of the use of computer models to understand weather and climate, and a detailed look at the current state of computer modelling, especially how global climate models are tested, what kinds of experiments are performed with them, how scientists know they can trust the models, and how they deal with uncertainty. The course will also explore the role of computer models in helping to shape society’s responses to climate change, in particular, what they can (and can’t) tell us about how to make effective decisions about government policy, international treaties, community action and the choices we make as individuals. The course will take a cross-disciplinary approach to these questions, looking at the role of computer models in the physical sciences, environmental science, politics, philosophy, sociology and economics of climate change. However, students are not expected to have any specialist knowledge in any of these fields prior to the course.

If all goes well, I plan to include some hands-on experimentation with climate models, perhaps using EdGCM (or even CESM if I can simplify the process of installing it and running it for them). We’ll also look at how climate models are perceived in the media and blogosphere (that will be interesting!) and compare these perceptions to what really goes on in climate modelling labs. Of course, the nice thing about a small seminar course is that I can be flexible about responding to the students’ own interests. I’m really looking forward to this…

New grad course: Climate Change Informatics

10. March 2010 · 4 comments · Categories: collaborative science, courses, software tools

I’m proposing a new graduate course for our department, to be offered next January (after I return from sabbatical). For the course calendar, I’m required to describe it in fewer than 150 words. Here’s what I have so far:

Climate Change Informatics

This introductory course will explore the contribution of computer science to the challenge of climate change, including: the role of computational models in understanding earth systems, the numerical methods at the heart of these models, and the software engineering techniques by which they are built, tested and validated; challenges in management of earth system data, such as curation, provenance, meta-data description, openness and reproducibility; tools for communication of climate science to broader audiences, such as simulations, games, educational software, collective intelligence tools, and the challenges of establishing reputation and trustworthiness for web-based information sources; decision-support tools for policymaking and carbon accounting, including the challenges of data collection, visualization, and trade-off analysis; the design of green IT, such as power-aware computing, smart controllers and the development of the smart grid.

Here’s the rationale:

This is an elective course. The aim is to bring a broad range of computer science graduate students together, to explore how their skills and knowledge in various areas of computer science can be applied to a societal grand challenge problem. The course will equip the students with a basic understanding of the challenges in tackling climate change, and will draw a strong link between the students’ disciplinary background and a series of inter-disciplinary research questions. The course crosscuts most areas of computer science.

And my suggested assessment modes:

  • Class participation: 10%
  • Term Paper 1 (essay/literature review): 40%
  • Term Paper 2 (software design or implementation): 40%
  • Oral Presentation or demo: 10%

Comments are most welcome – the proposal has to get through various committees before the final approval by the school of graduate studies. There’s plenty of room to tweak it in that time.

Who needs climate data?

24. September 2009 · 3 comments · Categories: climate science, courses, software tools

I’m teaching our introductory software engineering course this term, for which the students will be working on a significant software development project over the term. The main aim of the course is to get the students thinking about and using good software development practices and tools, and we organise the term project as an agile development effort, with a number of small iterations during the term. The students have to figure out for  themselves what to build at each iteration.

For a project, I’ve challenged the students to design new uses for the Canadian Climate Change Senarios Network. This service makes available the data on possible future climate change scenarios from the IPCC datasets, for a variety of end users. The current site allows users to run basic queries over the data set, and have the results returned either as raw data, or in a variety of visualizations. The main emphasis is on regional scenarios for Canada, so the service offers some basic downscaling, and ability to couple the scenarios with other regional data sources, such as data from weather monitoring stations in the region. However, to use the current service, you need to know quite a bit about the nature of the data: it asks you which models you’re interested in; which years you want data for (assumes you know something about 30-year averages); which scenarios you want (assumes you know something about the standard IPCC scenarios); which region you want (in latitude and longitude); and which variables you want (assumes you know something about what these variables measure). The current design reflects the needs of the primary user group for which the service was developed – (expert) researchers working on climate impacts and adaptation.

The challenge for the students on my course is to extend the service for new user groups. For example, farmers who want to know something about likely effects of climate change on growing seasons, rainfall and heat stress in their local area. High school students studying climate and weather. Politicians who want to understand what the latest science tells us about the impacts of climate change on the constituencies they represent. Activists who want to present a simple clear message to policymakers about the need for policy changes. etc.

I have around 60 students on the course, working in teams of 4. I’m hoping that the various teams will come up with a variety of ideas for how to make this dataset useful to new user groups, and I’ve challenged them to be imaginative. But more suggestions are always welcome…

Designing your own Earth System Model

15. July 2009 · 8 comments · Categories: climate modeling, courses

Spurred on by Michael Tobis’s thoughts about building a readable/understandable climate model in Python, I dug up an old bookmark to a course offered in UMinn’s geology department on Designing your own “Earth System” Model. Really neat idea for a course. Now, with a bit of tweaking, I could set up a similar course here, but with a twist – we recruit a mix of CS and Physics students onto the course, and put them together in cross-disciplinary teams, each team building an earth system model of some kind (tapping into the domain knowledge of the physicists), using current CS and software engineering tools and techniques. And to keep Michael happy, they code it all in Python. Marks awarded based on understandability of the code.

In the longer term, we keep the models produced from each instance of the course, and get the next cohort of students to develop them further – the aim is to build them up to be full scale earth system models.

Update: Michael has posted some more reflections on this.

Interdisciplinary Graduate Studies

15. April 2009 · Write a comment · Categories: collaborative science, courses, philosophy, systems thinking · Tags: , , , ,

Having talked with some of our graduate students about how to get a more inter-disciplinary education while they are in grad school, I’ve been collecting links to collaborative grad programs at U of T:

The Dynamics of Global Change Doctoral Program, housed in the Munk Centre. The core course, DGC1000H is very interesting – it starts with Malcolm Gladwell’s Tipping Point book, and then tours through money, religion, pandemics, climate change, the internet and ICTs, and development. What a wonderful journey.

The Centre for the Environment runs a Collaborative Graduate Program (MSc and PhD) in which students take some environmental science courses in addition to satisfying the degree requirements of their home department. The core course for this program is ENV1001, Environmental Decision Making, and it also include an internship to get hands on experience with environmental problem solving.

The Knowledge Media Design Institute (KMDI) also has a collaborative doctoral program, perfect for those interested in design and evaluation of new knowledge media, with a strong focus on knowledge creation, social change, and community

Finally, the Centre for Global Change Science has a set of graduate student awards, to help fund grad students interested in global change science. Oh, and they have a fascinating seminar series, mainly focussed on climate science (all done for this year, but get on their mailing list for next years seminars).

Are there any more I missed?