I’ve been collecting examples of different types of climate model that students can use in the classroom to explore different aspects of climate science and climate policy. In the long run, I’d like to use these to make the teaching of climate literacy much more hands-on and discovery-based. My goal is to foster more critical thinking, by having students analyze the kinds of questions people ask about climate, figure out how to put together good answers using a combination of existing data, data analysis tools, simple computational models, and more sophisticated simulations. And of course, learn how to critique the answers based on the uncertainties in the lines of evidence they have used.

Anyway, as a start, here’s a collection of runnable and not-so-runnable models, some of which I’ve used in the classroom:

Simple Energy Balance Models (for exploring the basic physics)

General Circulation Models (for studying earth system interactions)

  • EdGCM – an educational version of the NASA GISS general circulation model (well, an older version of it). EdGCM provides a simplified user interface for setting up model runs, but allows for some fairly sophisticated experiments. You typically need to let the model run overnight for a century-long simulation.
  • Portable University Model of the Atmosphere (PUMA) – a planet Simulator designed by folks at the University of Hamburg for use in the classroom to help train students interested in becoming climate scientists.

Integrated Assessment Models (for policy analysis)

  • C-Learn, a simple policy analysis tool from Climate Interactive. Allows you to specify emissions trajectories for three groups of nations, and explore the impact on global temperature. This is a simplified version of the C-ROADS model, which is used to analyze proposals during international climate treaty negotiations.
  • Java Climate Model (JVM) – a detailed desktop assessment model that offers detailed controls over different emissions scenarios and regional responses.

Systems Dynamics Models (to foster systems thinking)

  • Bathtub Dynamics and Climate Change from John Sterman at MIT. This simulation is intended to get students thinking about the relationship between emissions and concentrations, using the bathtub metaphor. It’s based on Sterman’s work on mental models of climate change.
  • The Climate Challenge: Our Choices, also from Sterman’s team at MIT. This one looks fancier, but gives you less control over the simulation – you can just pick one of three emissions paths: increasing, stabilized or reducing. On the other hand, it’s very effective at demonstrating the point about emissions vs. concentrations.
  • Carbon Cycle Model from Shodor, originally developed using Stella by folks at Cornell.
  • And while we’re on systems dynamics, I ought to mention toolkits for building your own systems dynamics models, such as Stella from ISEE Systems (here’s an example of it used to teach the global carbon cycle).

Other Related Models

  • A Kaya Identity Calculator, from David Archer at U Chicago. The Kaya identity is a way of expressing the interaction between the key drivers of carbon emissions: population growth, economic growth, energy efficiency, and the carbon intensity of our energy supply. Archer’s model allows you to play with these numbers.
  • An Orbital Forcing Calculator, also from David Archer. This allows you to calculate what the effect changes in the earth’s orbit and the wobble on its axis have on the solar energy that the earth receives, in any year in the past of future.

Useful readings on the hierarchy of climate models

A high school student in Ottawa, Jin, writes to ask me for help with a theme on the question of whether global warming is caused by human activities. Here’s my answer:

The simple answer is ‘yes’, global warming is caused by human activities. In fact we’ve known this for over 100 years. Scientists in the 19th Century realized that some gases in the atmosphere help to keep the planet warm by stopping the earth losing heat to outer space, just like a blanket keeps you warm by trapping heat near your body. The most important of these gases is Carbon Dioxide (CO2). If there were no CO2 in the atmosphere, the entire earth would be a frozen ball of ice. Luckily, that CO2 keeps the planet at the temperatures that are suitable for human life. But as we dig up coal and oil and natural gas, and burn them for energy, we increase the amount of CO2 in the atmosphere and hence we increase the temperature of the planet. Now, while scientists have known this since the 19th century, it’s only in the last 30 years that scientists were able to calculate precisely how fast the earth would warm up, and which parts of the planet would be affected the most.

Here are three really good explanations, which might help you for your theme:

  1. NASA’s Climate Kids website:
    http://climatekids.nasa.gov/big-questions/
    It’s probably written for kids younger than you, but has really simple explanations, in case anything isn’t clear.
  2. Climate Change in a Nutshell – a set of short videos that I really like:
    http://www.planetnutshell.com/climate
  3. The IPCC’s frequently asked question list. The IPCC is the international panel on climate change, whose job is to summarize what scientists know, so that politicians can make good decisions. Their reports can be a bit technical, but have a lot more detail than most other material:
    http://www.ipcc.ch/publications_and_data/ar4/wg1/en/faqs.html

Also, you might find this interesting. It’s a list of successful predictions by climate scientists. One of the best ways we know that science is right about something is that we are able to use our theories to predict what while happen in the future. When those predictions turn out to be correct, it gives us a lot more confidence that the theories are right: http://www.easterbrook.ca/steve/?p=3031

By the way, if you use google to search for information about global warming or climate change, you’ll find lots of confusing information, and different opinions. You might wonder why that is, if scientists are so sure about the causes of climate change. There’s a simple reason. Climate change is a really big problem, one that’s very hard to deal with. Most of our energy supply comes from fossil fuels, in one way or another. To prevent dangerous levels of warming, we have to stop using them. How we do that is hard for many people to think about. We really don’t want to stop using them, because the cheap energy from fossil fuels powers our cars, heats our homes, gives us cheap flights, powers our factories, and so on.

For many people it’s easier to choose not to believe in global warming than it is to think about how we would give up fossil fuels. Unfortunately, our climate doesn’t care what we believe – it’s changing anyway, and the warming is accelerating. Luckily, humans are very intelligent, and good at inventing things. If we can understand the problem, then we should be able to solve it. But it will require people to think clearly about it, and not to fool themselves by wishing the problem away.

A few weeks back, Randall Munroe (of XKCD fame) attempted to explain the parts of a Saturn V rocket (“Up Goer Five”) using only the most common one thousand words of English. I like the idea, but found many of his phrasings awkward, and some were far harder to understand than if he’d used the usual word.

Now there’s a web-based editor that let’s everyone try their hand at this, and a tumblr of scientists trying to explain their work this way. Some of them are brilliant, but many almost unreadable. It turns out this is much harder than it looks.

Here’s mine. I cheated once, by introducing one new word that’s not on the list, although it’s not really cheating because the whole point of science education is to equip people the right words and concepts to talk about important stuff:

If the world gets hotter or colder, we call that ‘climate’ change. I study how people use computers to understand such change, and to help them decide what we should do about it. The computers they use are very big and fast, but they are hard to work with. My job is to help them check that the computers are working right, and that the answers they get from the computers make sense. I also study what other things people want to know about how the world will change as it gets hotter, and how we can make the answers to their questions easier to understand.

[Update] And here’s a few others that I think are brilliant:

Emily S. Cassidy, Environmental Scientist at University of Minnesota:

In 50 years the world will need to grow two times as much food as we grow today. Meeting these growing needs for food will be hard because we need to make sure meeting these needs doesn’t lead to cutting down more trees or hurting living things. In the past when we wanted more food we cut down a lot of trees, so we could use the land. So how are we going to grow more food without cutting down more trees? One answer to this problem is looking at how we use the food we grow today. People eat food, but food is also used to make animals and run cars. In fact, animals eat over one-third of the food we grow. In some places, animals eat over two-thirds of the food grown! If the world used all of the food we grow for people, instead of animals and cars, we could have 70% more food and that would be enough food for a lot of people!

Anthony Finkelstein, at University College London, explaining requirements analysis:

I am interested in computers and how we can get them to do what we want. Sometimes they do not do what we expect because we got something wrong. I would like to know this before we use the computer to do something important and before we spend too much time and money. Sometimes they do something wrong because we did not ask the people who will be using them what they wanted the computer to do. This is not as easy as it sounds! Often these people do not agree with each other and do not understand what it is possible for the computer to do. When we know what they want the computer to do we must write it down in a way that people building the computer can also understand it.

This week, I start teaching a new grad course on computational models of climate change, aimed at computer science grad students with no prior background in climate science or meteorology. Here’s my brief blurb:

Detailed projections of future climate change are created using sophisticated computational models that simulate the physical dynamics of the atmosphere and oceans and their interaction with chemical and biological processes around the globe. These models have evolved over the last 60 years, along with scientists’ understanding of the climate system. This course provides an introduction to the computational techniques used in constructing global climate models, the engineering challenges in coupling and testing models of disparate earth system processes, and the scaling challenges involved in exploiting peta-scale computing architectures. The course will also provide a historical perspective on climate modelling, from the early ENIAC weather simulations created by von Neumann and Charney, through to today’s Earth System Models, and the role that these models play in the scientific assessments of the UN’s Intergovernmental Panel on Climate Change (IPCC). The course will also address the philosophical issues raised by the role of computational modelling in the discovery of scientific knowledge, the measurement of uncertainty, and a variety of techniques for model validation. Additional topics, based on interest, may include the use of multi-model ensembles for probabilistic forecasting, data assimilation techniques, and the use of models for re-analysis.

I’ve come up with a draft outline for the course, and some possible readings for each topic. Comments are very welcome:

  1. History of climate and weather modelling. Early climate science. Quick tour of range of current models. Overview of what we knew about climate change before computational modeling was possible.
  2. Calculating the weather. Bjerknes’ equations. ENIAC runs. What does a modern dynamical core do? [Includes basic introduction to thermodynamics of atmosphere and ocean]
  3. Chaos and complexity science. Key ideas: forcings, feedbacks, dynamic equilibrium, tipping points, regime shifts, systems thinking. Planetary boundaries. Potential for runaway feedbacks. Resilience & sustainability. (way too many readings this week. Have to think about how to address this – maybe this is two weeks worth of material?)
    • Liepert, B. G. (2010). The physical concept of climate forcing. Wiley Interdisciplinary Reviews: Climate Change, 1(6), 786-802.
    • Manson, S. M. (2001). Simplifying complexity: a review of complexity theory. Geoforum, 32(3), 405-414.
    • Rind, D. (1999). Complexity and Climate. Science, 284(5411), 105-107.
    • Randall, D. A. (2011). The Evolution of Complexity In General Circulation Models. In L. Donner, W. Schubert, & R. Somerville (Eds.), The Development of Atmospheric General Circulation Models: Complexity, Synthesis, and Computation. Cambridge University Press.
    • Meadows, D. H. (2008). Chapter One: The Basics. Thinking In Systems: A Primer (pp. 11-34). Chelsea Green Publishing.
    • Randers, J. (2012). The Real Message of Limits to Growth: A Plea for Forward-Looking Global Policy, 2, 102-105.
    • Rockström, J., Steffen, W., Noone, K., Persson, Å., Chapin, F. S., Lambin, E., Lenton, T. M., et al. (2009). Planetary boundaries: exploring the safe operating space for humanity. Ecology and Society, 14(2), 32.
    • Lenton, T. M., Held, H., Kriegler, E., Hall, J. W., Lucht, W., Rahmstorf, S., & Schellnhuber, H. J. (2008). Tipping elements in the Earth’s climate system. Proceedings of the National Academy of Sciences of the United States of America, 105(6), 1786-93.
  4. Typology of climate Models. Basic energy balance models. Adding a layered atmosphere. 3-D models. Coupling in other earth systems. Exploring dynamics of the socio-economic system. Other types of model: EMICS; IAMS.
  5. Earth System Modeling. Using models to study interactions in the earth system. Overview of key systems (carbon cycle, hydrology, ice dynamics, biogeochemistry).
  6. Overcoming computational limits. Choice of grid resolution; grid geometry, online versus offline; regional models; ensembles of simpler models; perturbed ensembles. The challenge of very long simulations (e.g. for studying paleoclimate).
  7. Epistemic status of climate models. E.g. what does a future forecast actually mean? How are model runs interpreted? Relationship between model and theory. Reproducibility and open science.
    • Shackley, S. (2001). Epistemic Lifestyles in Climate Change Modeling. In P. N. Edwards (Ed.), Changing the Atmosphere: Expert Knowledge and Environmental Government (pp. 107-133). MIT Press.
    • Sterman, J. D., Jr, E. R., & Oreskes, N. (1994). The Meaning of Models. Science, 264(5157), 329-331.
    • Randall, D. A., & Wielicki, B. A. (1997). Measurement, Models, and Hypotheses in the Atmospheric Sciences. Bulletin of the American Meteorological Society, 78(3), 399-406.
    • Smith, L. a. (2002). What might we learn from climate forecasts? Proceedings of the National Academy of Sciences of the United States of America, 99 Suppl 1, 2487-92.
  8. Assessing model skill - comparing models against observations, forecast validation, hindcasting. Validation of the entire modelling system. Problems of uncertainty in the data. Re-analysis, data assimilation. Model intercomparison projects.
  9. Uncertainty. Three different types: initial state uncertainty, scenario uncertainty and structural uncertainty. How well are we doing? Assessing structural uncertainty in the models. How different are the models anyway?
  10. Current Research Challenges. Eg: Non-standard grids – e.g. non-rectangular, adaptive, etc; Probabilistic modelling – both fine grain (e.g. ECMWF work) and use of ensembles; Petascale datasets; Reusable couplers and software frameworks. (need some more readings on different research challenges for this topic)
  11. The future. Projecting future climates. Role of modelling in the IPCC assessments. What policymakers want versus what they get. Demands for actionable science and regional, decadal forecasting. The idea of climate services.
  12. Knowledge and wisdom. What the models tell us. Climate ethics. The politics of doubt. The understanding gap. Disconnect between our understanding of climate and our policy choices.