Syllabus

Syllabus#

Key logistical info#

  • Class name and numbers: EAS 48800 / EAS B8800 / SUS 7300B: Climate and Climate Change

  • Credits: 3 credits for EAS 48800 and SUS 7300B; 4 credits for EAS B8800

  • Class Times: Wednesdays, 2-4:30pm; full schedule here

  • Location: Marshak 829

  • Final exam date and time: Wednesday, May 21st, 1-3:15pm

  • Required textbook: Global Warming Science by Eli Tziperman

  • Prerequisites: They technically differ across the 488, B88, and SUS7300B sections, but basically one semester of college math, one semester of college physics, one previous Earth science course

  • CCNY EAS Discord Join for announcements of events, internship opportunities, and much more

  • CCNY EAS student lounge: MR 902 (directly across from EAS main office in MR 926). Open to all students in EAS courses to study, relax, etc.

Course goal#

You, the student, will gain deep understanding of:

  1. the physical effects of climate change

  2. the physical causes of climate change

  3. the magnitudes and sources of the uncertainties in those causes and effects

As such, by semester’s end you will be much better equipped to assess claims and make decisions about climate change that you’ll face in life moving forward.[1]

More on prerequisites#

(TODO: calculus, python)

Course materials#

Required textbook#

Global Warming Science: A Quantitative Introduction to Climate Change and its Consequences by Eli Tziperman.

Warning

This book is mandatory for all students. You must purchase or otherwise acquire a copy of it at the start of the semester. It can be new or used and can be paperback, hardcover, or Kindle/digital.

The link above is to the official publisher’s page. Here are some other purchasing options:

As of Feb. 4, 2025, the cheapest option is to purchase the 180-day access from any of the above CCNY Textbookx links, which are $32.64. On Amazon there are used paperback options for as little as $34.98, and the Kindle/digital version is $44.

Note

If a one-time purchase of $32.64 would constitute a true burden to you, meaning it would seriously degrade your ability to get by, then please make use of the CCNY resources below relating to assistance for financial hardship. Unfortunately, I do not have access to any free or cheaper versions of the textbook.

Course Brightspace site#

Announcements and other key materials will be posted on the course Brightspace site.

Warning

Please sign up for email notifications of Brightspace announcements for this course. This will be the main way that important things, including HW assignments, exam information, and other time-sensitive matters will be communicated to you. If it has been announced on Brightspace, the professor will count on you having read it by no later than the next day.

Course public website#

This syllabus, the course schedule, links to lecture slides, and some supplementary materials will be posted on this public website (the one you’re on right now).

Course AllDayTA site#

I am trying out a new AI Teaching Assistant service called AllDayTA for this course. In short, it is a chatbot that’s available to you 24/7 to answer any questions you might have about the class. You can even ask it questions in languages besides Enlgish, and it will respond in that language! Here is their FAQ page for students. The link to you’ll use for our course is here: https://app.alldayta.com/cuny-city-college-of-ny/climate-and-climate-change.

Warning

The professor is able to see every query that students pose to AllDayTA, although they are anonymous: it doesn’t tell me who wrote each question. AllDayTA also provides the professor with an automated weekly summary of the questions posed, in order to highlight key points of confusion for students. Just keep that in mind. Here is AllDayTa’s privacy policy. If you object to using AllDayTA for privacy or other reasons, no problem at all.

Note

How it works behind the scenes, if you’re curious: I upload course material such as the syllabus, schedule, lecture slides, assignments, etc., and then it “learns” those materials and uses them to answer your questions. So far, I have uploaded the syllabus, schedule, jupyterhub instructions, and copies of the Brightspace announcements and assignment prompts. I will continue uploading it updated versions of each of these, along with lecture content and other class materials as the semester goes on.

There will be an early assignment through Brightspace to verify that you can login to this tool and to get you to start playing around with it. I encourage you to then continue using it throughout the semester! As the weeks progress and I add more and more content to the system, it should become more and more helpful to you and more able to help you with questions on homework assignments, things that confused you in class, and more.

Warning

AllDayTA is specifically designed to be as accurate as possible regarding the course materials, but like any generative AI tool it can still occasionally hallucinate, which is a fancy way of saying that the AI tells you wrong information. Fortunately, however, unlike some other AI tools, AllDayTA always explicitly cites the material that it has drawn its answers from.

Given all that, always check what it tells you against the source material it cites. This is a specific example of a more general, critical skill in working with generative AI (and humans for that matter): you cannot assume that what it says is 100% accurate.

Grading#

The grading breakdown is listed in the table below. For those enrolled in this class as EAS 48800 or SUS 7300B, this course is 3 credits. For those enrolled in B8800, it is 4 credits, with the 4th credit coming through a term project covered here.

Note that under Midterm and Final, there are two numbers listed in either case. You will automatically be assigned the grading scheme that gives you the better grade. The idea is that, if you did poorly on the midterm, you can make up for it by doing better on the final, with the midterm score then being essentially dropped.

Category

Weight, EAS48800 & SUS7300B

Weight, EAS B8800

Homework

15%

15%

Midterm

35% or 0%

25% or 0%

Final exam

50% or 85%

40% or 75%

Term project

0%

20%

There is no pre-set distribution of grades or “curve” across the class: you are not competing against your classmates.

Exam scores will be curved but in way that benefits all students: the highest score will be counted as a 100%, and all other scores will be adjusted up accordingly. (That way, if the exam accidentally ends up being much more difficult than anticipated because of how it was written, the class won’t be effectively penalized.)

Expectations#

Readings#

Assigned readings from the textbook are mandatory, not optional/supplemental. They are posted on the course schedule. For all class meetings after the very first one, I will expect that you have completed the assigned reading prior to arriving at class that day.

That said, the midterms and final exam will focus almost entirely on material we cover directly in class. So spend your time studying accordingly.

Attendance#

You all are adults who have voluntarily signed up for this course, which is an elective not a requirement. Out of respect for your autonomy as individual adults, I am not going to take attendance, and attendance by itself has no influence on your grade. After all, if you master all the material, you deserve an A, regardless of how often you were in class.

That said, regular attendance is strongly encouraged, for several reasons:

  • With only one 2.5 hour meeting per week, and fourteen total over the semester, missing any single one amounts to missing \(1/14\approx7\%\) of the whole course, which is a lot.

  • The 2nd half of every class will be interactive, often with you working alone or in pairs on HW assignments via the course JupyterHub. You will be responsible for completing those assignments regardless, and so it’s much better to get going on them in-person where you can ask the professor and your classmates for help as needed.

  • Because it’s fun! We’re really trying to build a sense of community in this class over the course of the semester, a band of students committed to investigating this monumentally important topic using both some of history’s most elegant physical equations and some of the cutting-edge computational and AI tools. That requires a critical mass of people, attention, and energy in the room each class!

  • Lectures will not generally be recorded or zoomed.

Punctuality#

Show up on time, meaning seated and ready to start learning by 2pm when class starts. Starting after the first class, I will start class pretty much right on time.

We will always take a 5-10 minute break roughly halfway through the 2.5 hour class period to give you an opportunity to stretch, use the restroom, reset mentally, etc. Given that, we can’t afford to lose an additional 5-10 minutes every class by starting late: there are 14 class meetings, and so losing even 5 minutes from each amounts to (14 meetings) x (5 minutes lost / meeting) = 70 minutes lost, which is basically a whole half of a course period.

Of the six elevators in Marshak, on any given day usually at least one, and sometimes three or four, are out of service, and so it can take several minutes for one to arrive and carry you all the way up to the 8th floor. Plan accordingly.

If you have a tight turnaround in your schedule due to work, another class, or some other important, repeating commitment just before this class, please let me know as soon as possible.

Homework#

HW assignments are graded on an effort basis, 0% to 100%. An earnest, thoughtful attempt at every problem will earn you a 95%. Truly exceptional work will earn a 100%. Any question not answered receives 0%. Questions partially answered or attempted without sufficient critical thinking will earn partial credit.

All HW due dates will be posted on the course schedule.

Late policy: Extensions require pre-approval in writing by the professor; email me ASAP if you get sick or have some major conflict. Otherwise, 50% reduction for submissions the day after the deadline, zero credit for submissions after the next day.

In total, the homework assignments are worth 15% of your semester grade. This percentage reflects a balance: because of generative AI tools, I cannot meaningfully evaluate your comprehension of the material from take-home HW assignments. However, working through problem sets is a crucial way of improving your understanding of this material!

Approach your assignments accordingly: sure, in a pinch you can feed everything to ChatGPT and get most or all of that 15% of the grade…but how likely is it that your exam scores—which make up 85% for non-B88 students and 65% for B88 students—won’t suffer even more if you don’t put in the effort to understand the problem sets? Keep in mind, the exams will follow the problem sets closely.

Course philosophy#

“Climate change” vs. “climate science”#

This is a class about the physical science of climate change. The science of climate change is a subset of the broader topic of climate science, and truly understanding many aspects of climate change requires somewhat deep dives into the underlying fundamental climate science. However, this underlying science will always be on an as-needed basis in order to understand present-day and anticipated climate change due to human activities.

Structure of weekly class meetings#

First 5-10 minutes: exam prep question#

Each class will start with an exam prep question. These questions closely mimic the types of questions that will appear on the midterm and final.

For each one, you will be given a few minutes to work through it, and it will be specified whether to work on it alone or in pairs. Then, we’ll go through the solution together.

It’s possible that one of these exact questions will appear on the midterm and/or final…though not more than one! Take these seriously but don’t obsess over them at the expense of reviewing other material. For similar reasons, there’s no point in just plugging them into Google or ChatGPT…try them 100% on your own, and then if you’re stuck after a minute or two, consult your textbook and/or notes.

Exam prep questions assume you have done the assigned reading! Often they will be based on the textbook material that you were assigned for that day. (Though sometimes they’ll be from earlier material.) You’ll get much less out of them if you haven’t done the reading

These will be the very first thing we do at 2pm when class starts…further motivation, I hope, to show up on time and ready to think critically.

Main “lecture” (~1 hr 15 min): deep dive into key and/or challenging concepts from the reading#

After the exam prep question exercise and any miscellany (announcements etc.), we’ll take 1 hr or up to 1.5 hrs for the main portion of the class. Because you will have already completed the reading, this will not be a traditional lecture that comprehensively covers the material.

Instead, it will usually consist of:

  1. a quick summary of what the most important parts of the reading were

  2. a deep dive into one or more important and/or challenging concepts from the reading

  3. an opportunity for you to ask any other questions

During this part of class, you should be paying attention: not on your phone, not logged into the lab computers or using your own laptop, etc. To keep us all engaged, I will sometimes cold-call on people.

Stretch break (~5 min) while I post that week’s assignment#

2.5 hours is too long without a break! Get up, stretch your legs, use the restroom, whatever you need to be in the best mindset and ready physically to make the most of the second portion of class.

During that time, I will post that week’s HW assignment to Brightspace and, if it has a JupyterHub component, on the course JupyterHub.

Final ~1 hour: hands-on session#

In this part of the class, I’ll mostly assist you with the computational aspects of the course. This will often consist of an initial, guided tutorial followed by a more open session where I’ll be floating around the classroom answering questions as you all ask them.

Importantly, this class always spans a wide range of prior technical knowledge, from those who have never written a line of computer code in their lives to those who are daily active users of advanced data analysis packages in Python or other languages. To keep from wasting the time of the more advanced students, you can consider the tutorials/hands-on part here optional. You can, if you’d like, immediately proceed to the HW assignment that was just posted. However, please do not leave early, so as not to disturb the learning environment for others.

Integrating artificial intelligence#

Summary: this class will incorporate generative AI in lots of ways#

Currently, in early 2025, we are in the midst of a revolution in the abilities and usage of artificial intelligence (AI) models, most of all, large language models (LLMs) such as ChatGPT. These have grown in power, capabilities, and popularity at an insane pace over the last couple years. There is every indication that they will continue to do so into the future.

This has two major implications for this course, the first being something you’ve surely heard/experienced in other courses, and the second being a bit more radical.

  1. The ubiquity and power of generative AI tools changes the way that assessment and grading have to be done. At least in this course, the philosophy of grading is that your grade reflect what you personally have learned. As such, grading has to put less weight on those things like take-home HW problem sets and essays that generative AI can do decently (or even quite well) without you giving it more than a few moments thought. Conversely, grading has to put more weight on means of assessment like in-person, pencil-and-paper exams that test the comprehension within your brain rather than ChatGPT.

  2. Nevertheless, your professor strongly believes that, considering the professional lives that each of you are currently preparing for, using generative AI tools effectively will be among the single most important skills determining your productivity and thus employment prospects. (Consider the increase in the capabilities of these tools in just two years since ChatGPT3 was launched, and then consider that your professional life will span, hopefully, three or four decades…imagine how much better the models will get, how many more of the tasks they’ll be able to do that, right now, people doing the jobs you’re working toward do themselves.) As such, the course will integrate generative AI tools in various ways. In part because the landscape of those tools is changing so rapidly, the precise details there will be fleshed out as the semester goes on.

This class is an experiment!#

That’s true in several ways, which I’ll list immediately below. The upshot for you is: please be patient but also communicative whenever things don’t go according to plan…I am trying to turn this into a dynamic, cutting-edge course in all aspects, which necessarily entails a fair amount of experimentation. And experiments can always go one way or another :)

One is that I am totally revamping the course’s scientific content compared to how it’s been taught in this department for many years, including the one previous time I taught it last Spring. Traditionally courses like this within Earth science or similar departments have had roughly the opposite approach: teach the fundamental underlying climate science, without regard to climate change, and then bring in the climate change problem at the end. For a long time, this made sense, when the students enrolling in these courses were mostly those preparing for future careers in scientific research on climate, and the problem of climate change was less severe physically and less predominant societally. But times have changed. Understandably, most of you care much more about how climate is already changing and might change in the future.

Another is due to the integration of generative AI tools as noted above. This puts us in rapidly changing, sometimes fraught territory. Let’s do our best together to navigate it optimally with respect to your learning outcomes.

Another is due to the use of JupyterHub-based notebooks as a primary tool. This is my first time using these, and there may well be some technical hiccups there.

On incorporating non-physical science considerations#

You’ve surely heard various claims in the news, on social media, or by people you know about climate change. A common one is “climate change is making X worse,” where “X” here is a stand in for some phenomenon—hurricanes, droughts, heat waves, unemployment, etc. One major goal of this course is to leave you well-informed about how to engage with claims like these.

Claims like these can usefully put into two broad categories. First: things that have already happened, starting from the early 20th century through the present. Second: the projections of future climate change in the remainder of the 21st century. This is something like the span of generations from your great-grandparents to your own future great-grandchildren.

Hypothetical examples of each type of claim (note these are not necessarily scientifically valid, just meant to illusrate the point):

  • historical climate change: “Climate change made Hurricane Sandy 20% stronger”

  • future climate change: “Because of climate change, droughts in the Amazon rainforest will be 50% more frequent by 2050”

In both cases—historical climate change and projected future climate change—there are many aspects of the problem we are very confident in. But there are also others that we are highly unsure of. For the future projections, much of that uncertainty comes not from the physical science but from societal outcomes: how quickly do renewable energy technologies improve? Does an effective international climate policy get enacted soon? Is geoengineering widely deployed? etc.

Broadly, we will start with the physical aspects of climate change that are most certain, and as the course progresses we will work our way to increasingly more uncertain topics. So, we’ll start with the direct effect of increasing carbon dioxide in the atmosphere on radiative transfer, and how that acts to warm the lowest layer of Earth’s atmosphere including the surface. Understanding the temperature response to climate change is doubly important, because not only is it important for its own sake, but as we’ll see later so many of the other impacts of climate change approximately scale with the globally averaged temperature change.

But in parallel with the physical science, we will explore the societal aspects of climate change. To be clear, this is a class on the physical science of climate change, not one on climate policy, law, or other more social-scientific aspects of climate change. However, as we will see, being able to effectively evaluate claims made about and projections of how the climate is changing and will change requires us, in 2025, to bring in these non-hard-science topics. We’ll find that these are the most uncertain of all, and in some ways are more than that, they’re intrinsically unanswerable.

This certainly requires some social science: economics, political science, psychology, sociology. But it also requires philosophy and ethics. Questions like, “What do we owe the unborn?” Should we prioritize helping the greatest number of people, or helping those who most need it, or punishing those most responsible, or something else? The answer one arrives at to these types of questions inherently involves values, whether we’re aware of that or not, and as such these questions fall into the realm of philosophy and ethics.

Broadly speaking, you could say that regarding the physical science material, we will both ask questions and largely provide answers—even if those answers include some uncertainty. Whereas regarding the more societal aspects, it is more about the asking of the questions themselves.

Discussion of politically controversial topics#

Climate change has become a deeply political topic, and we will not shy away from the resulting controversies. You are encouraged to play the part of the “devil’s advocate,” even for ideas that initially seem obvious. You can even do this explicitly, by saying to the instructor or a classmate, “I’m playing devil’s advocate here, but what if someone thought X instead?” You will never be penalized for disagreeing with or questioning something the instructor classmate says, as long as it is done respectfully.

Borrowing from others, here are guidelines for these kinds of discussions:

  • Bring light, not heat.

  • Treat every member of the class with respect, even if you disagree with their opinion.

  • Respecting others’ opinions doesn’t mean giving up your own.

  • No ideas are immune from scrutiny and debate.

  • That includes those stated by your professor!

The instructor will not advocate for or against any particular societal response to climate change, whether political, economic, technological, philosophical, or anything else. (Students will of course have the right to do so, whatever their positions.) That said, demographically at CCNY in Spring 2025 in a class on Climate Change that each of you enrolled in as an elective, it is extremely likely that a considerable majority of you hold a fairly progressive outlook regarding climate change. As a result, the instructor will deliberately emphasize arguments that challenge that perspective, meaning those that are more market-oriented and wary of tradeoffs as regards overall human flourishing in the context of climate change and emissions reductions. This does not constitute the course advocating for any particular ideological stance. If it was likely or empirically determined that the class makeup was in fact predominantly right-leaning as regards climate change, the instructor would take the exact opposite tack, emphasizing arguments coming from a more progressive/activist bent. One goal is for the semester to end without you having any good idea of what the instructor’s own actual beliefs are about these issues.

Summing that all up: whatever your views are about climate change coming in, this class hopefully will challenge those views in some meaningful way!

Academic Integrity#

I encourage you to use generative AI tools such as ChatGPT as a tool to advance your learning, not as a crutch. You simply will not learn the material if you uncritically ask ChatGPT to do your assignments for you—and your exam scores will reflect that. But it is possible to use in a way that helps you really understand the material.

More generally: don’t cheat. You are required to follow CCNY’s Community Standards and the CUNY Policy on Academic Integrity. All students will be held fully accountable to these rules and disciplinary procedures.

CCNY/CUNY resources available to you#

I strongly recommend each of you to review this list of resources and take note of any that may be helfpul to you either right now or in the future. Do not hesitate to make use of them!

Financial, housing, and food security#

Academic and professional#

Health and accessability#

International#

Safety#