• BIG100: “The end of the world as we know it”;
• BIG101: “Energy: From Fire to the Future”;
• BIG102: “The Internet: Saving Civilization or Trashing the Planet?”

I’m delighted to be teaming up with Prof Miriam Diamond from Earth Sciences and Prof Pamela Klassen from Study of Religion to teach BIG102. Our aim is to give students some understanding of how the technologies that drive the internet work, and then to explore how the internet has reshaped the way we use information, our knowledge and beliefs about the world, and the impact that creating (and disposing of) internet technologies has on the environment, on the economy, and on the dynamics of innovation. A key goal is to foster critical thinking and information literacy skills, and especially to be able to think about and analyze a complex system-of-systems from different perspectives.

For the first term, we’re planning to cover a broad set of provocative questions, to get students thinking about the internet from different perspectives:

1. What is a big idea? (A course introduction, and a primer on trans-disciplinary thinking)
2. Who invented the internet? (Myths about the internet, and why they stick)
3. How does the internet work? (An introduction to some of the key technologies)
4. How new is the internet? (A short history of communications technologies, to put the internet in its historical context)
5. Has the internet changed us? (We’ll explore in particular, how the internet is transforming universities and learning)
6. What is the environmental footprint of the internet? (An initial assessment of energy consumption, resource extraction, and waste disposal)
7. Does the internet make us smarter? (An exploration of how internet search works, and how it affects our approaches to problem-solving)
8. Is the internet a time-saver or time-waster? (How the internet offers endless distractions, blurs distinctions between work and leisure, and its overall effect on productivity)
9. Can you be anonymous on the internet? (The idea of your information footprint – who’s keeping track of data about you, how they do it, and why)
10. Is the Internet a Cheater’s Paradise? (From plagiarism to adultery – how the internet facilitates cheating, new ways of discovering it, and virtual vigilante justice)
11. Who’s Not Online? (The idea of the digital divide, and the demographic and socio-economic factors that limit people’s access)
12. Gadgets as Gifts? (Just in time for the Christmas break, we’ll explore the environmental impact of our love of new gadgets, and whether there are sustainable alternatives)

In the second term, we plan to pick three themes to explore in more detail, so that we can explore inter-connections between some of these questions, and get the students engaged in independent research projects that synthesize what they’re learning:

1. The Internet and the Innovation Imperative.
   • Is the Internet Innovative? How Moore’s law has driven innovation; the dotcom boom and bust; and the current hype around new technologies such as 3D printing, sensor networks, and the semantic web.
   • What are the Resource Implications of the Internet? We’ll use material flow analysis to explore extraction and disposal and likely shortages of strategic minerals, and the geo-political implications of attempting to feed an exponential growth in demand.
   • The Environmental and Human Health Burden of the Internet. Building on the discussion of resource implications, we’ll look at the health implications of mineral extraction and e-waste disposal, and the burden this places on people and ecosystems, especially in poorer countries.
   • What is the Opportunity Cost of the Internet? Does investment in internet innovation mean we’re underinvesting in other things (eg clean energy, transport, social innovation). Have we developed an over-optimistic belief that IT technologies can solve all problems?
2. The Internet, Democracy, and Security.
   • Censorship & Internet Governance. How much power do governments have to control what happens on the internet? Does the internet enhance or undermine democracy?
   • The Underbelly of the Internet: Hackers, Espionage, and Trolls. How internet systems can be exploited by different groups, for example by crime syndicates who break into secure systems, by political groups who use a web presence to spread misinformation, and by internet trolls who violate social norms to disrupt and intimidate online discussions.
   • Does the Internet make us a more open society? The open source movement and its successors (open government, creative commons, etc) are based on the idea that if everyone has access to the inner workings of systems, this removes barriers to participation, fosters creativity, and makes those systems better for everyone. But does it work?
   • Transnational Jurisdiction: Legal boundaries and the Internet. We’ll wrap up this theme with a question about who should police the internet.
3. The Internet, Communities, and Interpersonal Relationships
   • Does your Google-Brain make you forget? How has instant access to vast amounts of information changed our memories and our perceptions of ourselves? For example, does GPS route-finding mean we lose our ability to navigate and our sense of place? And what are the implications of the kind of personal digital archives that technologies such as Google Glass might allow us to create?
   • Can you find love on the Internet? An exploration of how the internet changes personal relationships, from the role of dating sites and virtual social networks, to the way that online porn affects our perceptions of gender roles and body image.
   • Can you find God on the Internet? How the internet affects religious communities, tolerance of different worldviews, and the very nature of faith.

Of course, this outline is still a draft – we’ll refine it over the next few months as we prepare for the first group of students in September.

We’re still exploring which textbooks to use, and even whether ‘books’ makes sense for a course like this – we’re hoping to make this a constructivist learning experience by using a variety of different internet-based media and information access tools throughout the course.  However, we’re currently evaluating these books:

• TechNO-fix, by Michael and Joyce Huesemann (a fascinating counter-point to the vast literature on how great technology is!);
• The Shallows: What the internet is doing to our brains: by Nicholas Carr
• Networked: The New Social Operating System, by Lee Rainie & Barry Wellman

Feel free to suggest other books and material!

I find this song fascinating, partly because of the weird mix of progressive rock and dubstep. But more for the lyrics:

All natural and technological processes proceed in such a way that the availability of the remaining energy decreases. In all energy exchanges, if no energy enters or leaves an isolated system, the entropy of that system increases. Energy continuously flows from being concentrated to becoming dispersed, spread out, wasted and useless. New energy cannot be created and high grade energy is destroyed. An economy based on endless growth is unsustainable. The fundamental laws of thermodynamics will place fixed limits on technological innovation and human advancement. In an isolated system, the entropy can only increase. A species set on endless growth is unsustainable.

This summarizes, perhaps a little too succinctly, the core of the critique of our current economy, first articulated clearly in 1972 by the Club of Rome in the Limits to Growth Study. Unfortunately, that study was widely dismissed by economists and policymakers. As Jorgen Randers points out in a 2012 paper, the criticism of the Limits to Growth study was largely based on misunderstandings, and the key lessons are absolutely crucial to understanding the state of the global economy today, and the trends that are likely over the next few decades. In a nutshell, humans exceeded the carrying capacity of the planet sometime in the latter part of the 20th century. We’re now in the overshoot portion, where it’s only possible to feed the world and provide energy for economic growth by consuming irreplaceable resources and using up environmental capital. This cannot be sustained.

In general systems terms, there are three conditions for sustainability (I believe it was Herman Daly who first set them out in this way):

1. We cannot use renewable resources faster than they can be replenished.
2. We cannot generate wastes faster than they can be absorbed by the environment.
3. We cannot use up any non-renewable resource.

We can and do violate all of these conditions all the time. Indeed, modern economic growth is based on systematically violating all three of them, but especially #3, as we rely on cheap fossil fuel energy. But any system that violates these rules cannot be sustained indefinitely, unless it is also able to import resources and export wastes to other (external) systems. The key problem for the 21st century is that we’re now violating all three conditions on a global scale, and there are no longer other systems that we can rely on to provide a cushion – the planet as a whole is an isolated system. There are really only two paths forward: either we figure out how to re-structure the global economy to meet Daly’s three conditions, or we face a global collapse (for an understanding of the latter, see GrahamTurner’s 2012 paper).

A species set on endless growth is unsustainable.

• M. Stockhause, H. Höck, F. Toussaint, and M. Lautenschlager, Quality assessment concept of the World Data Center for Climate and its application to CMIP5 data, Geosci. Model Dev., 5, 1023-1032, 2012.
  Describes the distributed quality control concept that was developed for handling the terabytes of data generated from CMIP5, and the challenges in ensuring data integrity (also includes a useful glossary in an appendix).
• B. N. Lawrence, V. Balaji, P. Bentley, S. Callaghan, C. DeLuca, S. Denvil, G. Devine, M. Elkington, R. W. Ford, E. Guilyardi, M. Lautenschlager, M. Morgan, M.-P. Moine, S. Murphy, C. Pascoe, H. Ramthun, P. Slavin, L. Steenman-Clark, F. Toussaint, A. Treshansky, and S. Valcke, Describing Earth system simulations with the Metafor CIM, Geosci. Model Dev., 5, 1493-1500, 2012.
  Explains the Common Information Model, which was developed to describe climate model experiments in a uniform way, including the model used, the experimental setup and the resulting simulation.
• S. Valcke, V. Balaji, A. Craig, C. DeLuca, R. Dunlap, R. W. Ford, R. Jacob, J. Larson, R. O’Kuinghttons, G. D. Riley, and M. Vertenstein, Coupling technologies for Earth System Modelling, Geosci. Model Dev., 5, 1589-1596, 2012.
  An overview paper that compares different approaches to model coupling used by different earth system models in the CMIP5 ensemble.
• S. Valcke, The OASIS3 coupler: a European climate modelling community software, Geosci. Model Dev., 6, 373-388, 2013 (See also the Supplement)
  A detailed description of the OASIS3 coupler, which is used in all the European models contributing to CMIP5. The OASIS User Guide is included as a supplement to this paper.

(Note: technically speaking, the call for papers for this issue is still open – if there are more software aspects of CMIP5 that you want to write about, feel free to submit them!)

The question of ‘owed’ or ‘committed’ warming arises because we know it takes some time for the planet to warm up in response to an increase in greenhouse gases in the atmosphere. You can calculate a first approximation of how much it will warm up from a simple energy balance model (like the ones I posted about last month). However, to calculate how long it takes to warm up you need to account for the thermal mass of the oceans, which absorb most of the extra energy and hence slow the rate of warming of surface temperatures. For this you need more than a simple energy balance model.

You can do a very simple experiment with a Global Circulation Model, by setting CO2 concentrations at double their pre-industrial levels, and then leave them constant at this level, to see how long the earth takes to reach a new equilibrium temperature. Typically, this takes several decades, although the models differ on exactly how long. Here’s what it looks like if you try this with EdGCM (I ran it with doubled CO2 concentrations starting in 1958):

Of course, the concentrations would never instantaneously double like that, so a more common model experiment is to increase CO2 levels gradually, say by 1% per year (that’s a little faster than how they have risen in the last few decades) until they reach double the pre-industrial concentrations (which takes approx 70 years), and then leave them constant at that level. This particular experiment is a standard way of estimating the Transient Climate Response - the expected warming at the moment we first reach a doubling of CO2 - and is included in the CMIP5 experiments. In these model experiments, it typically takes a few decades more of warming until a new equilibrium point is reached, and the models indicate that the transient response is expected to be a little over half of the eventual equilibrium warming.

This leads to a (very rough) heuristic that as the planet warms, we’re always ‘owed’ almost as much warming again as we’ve already seen at any point, irrespective of future emissions, and it will take a few decades for all that ‘owed’ warming to materialize. But, as Damon argued in his talk, there are two problems with this heuristic. First, it confuses the issue when discussing the need for an immediate reduction in carbon emissions, because it suggests that no matter how fast we reduce them, the ‘owed’ warming means such reductions will make little difference to the expected warming in the next two decades. Second, and more importantly, the heuristic is wrong! How so? Read on!

For an initial analysis, we can view the climate problem just in terms of carbon dioxide, as the most important greenhouse gas. Increasing CO2 emissions leads to increasing CO2 concentrations in the atmosphere, which leads to temperature increases, which lead to climate impacts. And of course, there’s a feedback in the sense that our perceptions of the impacts (whether now or in the future) lead to changed climate policies that constrain CO2 emissions.

So, what happens if we were to stop all CO2 emissions instantly? The naive view is that temperatures would continue to rise, because of the ‘climate commitment’  - the ‘owed’ warming that I described above. However, most models show that the temperature stabilizes almost immediately. To understand why, we need to realize there are different ways of defining ‘climate commitment’:

• Zero emissions commitment – How much warming do we get if we set CO2 emissions from human activities to be zero?
• Constant composition commitment – How much warming do we get if we hold atmospheric concentrations constant? (in this case, we can still have some future CO2 emissions, as long as they balance the natural processes that remove CO2 from the atmosphere).

The difference between these two definition is shown here. Note that in the zero emissions case, concentrations drop from an initial peak, and then settle down at a lower level:

The model experiments most people are familiar with are the constant composition experiments, in which there is continued warming. But in the zero emissions scenarios, there is almost no further warming. Why is this?

The relationship between carbon emissions and temperature change (the “Carbon Climate Response”) is complicated, because it depends two factors, each of which is complicated by (different types of) inertia in the system:

• Climate Sensitivity – how much temperature changes in response to difference levels of CO2 in the atmosphere. The temperature response is slowed down by the thermal inertia of the oceans, which means it takes several decades for the earth’s surface temperatures to respond fully to a change in CO2 concentrations.
• Carbon sensitivity – how much concentrations of CO2 in the atmosphere change in response to different levels of carbon emissions. A significant fraction (roughly half) of our CO2 emissions are absorbed by the oceans, but this also takes time. We can think of this as “carbon cycle inertia” – the delay in uptake of the extra CO2, which also takes several decades. [Note: there is a second kind of carbon system inertia, by which it takes tens of thousands of years for the rest of the CO2 to be removed, via very slow geological processes such as rock weathering.]

It turns out that the two forms of inertia roughly balance out. The thermal inertia of the oceans slows the rate of warming, while the carbon cycle inertia accelerates it. Our naive view of the “owed” warming is based on an understanding of only one of these, the thermal inertia of the ocean, because much of the literature talks only about climate sensitivity, and ignores the question of carbon sensitivity.

The fact that these two forms of inertia tend to balance leads to another interesting observation. The models all show an approximately linear response to cumulative emissions. For example, here are the CMIP3 models, used in the IPCC AR4 report (the average of the models, indicated by the arrow, is around 1.6C of warming per 1,000 gigatonnes of carbon):

The same relationship seems to hold for the CMIP5 models, many of which now include a dynamic carbon cycle:

This linear relationship isn’t determined by any physical properties of the climate system, and probably won’t hold in much warmer or cooler climates, nor when other feedback processes kick in. So we could say it’s a coincidental property of our current climate. However, it’s rather fortuitous for policy discussions.

Historically, we have emitted around 550 billion tonnes since the beginning of the industrial era, which gives us an expected temperature response of around 0.9°C. If we want to hold temperature rises to be no more than 2°C of warming, total future emissions should not exceed a further 700 billion tonnes of Carbon. In effect, this gives us a total worldwide carbon budget for the future. The hard policy question, of course, is then how to allocate this budget among the nations (or people) of the world in an equitable way.

[A few years ago, I blogged about a similar analysis, which says that cumulative carbon emissions should not exceed 1 trillion tonnes in total, ever. That calculation gives us a smaller future budget of less then 500 billion tonnes. That result came from analysis using the Hadley model, which has one of the higher slopes on the graphs above. Which number we use for a global target then might depend on which model we believe gives the most accurate projections, and perhaps how we also factor in the uncertainties. If the uncertainty range across models is accurate, then picking the average would give us a 50:50 chance of staying within the temperature threshold of 2°C. We might want better odds than this, and hence a smaller budget.]

In the National Academies report in 2011, the cumulative carbon budgets for each temperature threshold were given as follows (note the size of the uncertainty whiskers on each bar):

[For a more detailed analysis see: Matthews, H. D., Solomon, S., & Pierrehumbert, R. (2012). Cumulative carbon as a policy framework for achieving climate stabilization. Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 370(1974), 4365–79. doi:10.1098/rsta.2012.0064]

So, this allows us to clear up some popular misconceptions:

The idea that there is some additional warming owed, no matter what emissions pathway we follow is incorrect. Zero future emissions means little to no future warming, so future warming depends entirely on future emissions. And while the idea of zero future emissions isn’t policy-relevant (because zero emissions is impossible, at least in the near future), it does have implications for how we discuss policy choices. In particular, it means the idea that CO2 emissions cuts will not have an effect on temperature change for several decades is also incorrect. Every tonne of CO2 emissions avoided has an immediate effect on reducing the temperature response.

Another source of confusion is the emissions scenarios used in the IPCC report. They don’t diverge significantly for the first few decades, largely because we’re unlikely (and to some extent unable) to make massive emissions reductions in the next 1-2 decades, because society is very slow to respond to the threat of climate change, and even when we do respond, the amount of existing energy infrastructure that has to be rebuilt is huge. In this sense, there is some inevitable future warming, but it comes from future emissions that we cannot or will not avoid. In other words, political, socio-economic and technological inertia are the primary causes of future climate warming, rather than any properties of the physical climate system.

So here’s my draft pitch:

(1) Create a joint faculty position between the Department of Computer Science and the new School of Environment.

Last summer U of T’s Centre for Environment was relaunched as a School of Environment, housed wholly within the Faculty of Arts and Science. As a school, it can now make up to 49% faculty appointments. [The idea is that to do interdisciplinary research, you need a base in a home department/discipline, where your tenure and promotion will be evaluated, but would spend half your time engaged in inter-disciplinary research and teaching at the School. Hence, a joint position for us would be 51% CS and 49% in the School of Environment.]

A strong relationship between Computer Science and the School of Environment makes sense for a number of reasons. Most environmental science research makes extensive use of computational modelling as a core research tool, and the environmental sciences are one of the greatest producers of big data. As an example, the Earth System Grid currently stores more than 3 petabytes of data from climate models, and this is expected to grow to the point where by the end of the decade a single experiment with a climate model would generate an exabyte of data. This creates a number of exciting opportunities for application of CS tools and algorithms, in a domain that will challenge our capabilities. At the same time, this research is increasingly important to society, as we seek to find ways to feed 9 billion people, protect vital ecosystems, and develop strategies to combat climate change.

There are a number of directions we could go with such a collaboration. My suggestion is to pick one of:

• Climate informatics. A small but growing community is applying machine learning and data mining techniques to climate datasets. Two international workshops have been held in the last two years, and the field has had a number of successes in knowledge discovery that have established its importance to climate science. For a taste of what the field covers, see the agenda of the last CI Workshop.
• Computational Sustainability. Focuses on the decision-support needed for resource allocation to develop sustainable solutions in large-scale complex adaptive systems. This could be viewed as a field of applied artificial intelligence, but to do it properly requires strong interdisciplinary links with ecologists, economists, statisticians, and policy makers. This growing community has run run an annual conference, CompSust, since 2009, as well as tracks at major AI conferences for the last few years.
• Green Computing. Focuses on the large environmental footprint of computing technology, and how to reduce it. Energy efficient computing is a central concern, although I believe an even more interesting approach is when we take a systems approach to understand how and why we consume energy (whether in IT equipment directly, or in devices that IT can monitor and optimize). Again, a series of workshops in the last few years has brought together an active research community (see for example, Greens’2013),

(2) Hire more software engineering professors!

Our software engineering group is now half the size it was a decade ago, as several of our colleagues retired. Here’s where we used to be, but that list of topics and faculty is now hopelessly out of date. A decade ago we had five faculty and plans to grow this to eight by now. Instead, because of the hiring freeze and the retirements, we’re down to three. There were a number of reasons we expected to grow the group, not least because for many years, software engineering was our most popular undergraduate specialist program and we had difficulty covering all the teaching, and also because the SE group had proved to be very successful in bringing in research funding, research prizes, and supervising large numbers of grad students.

Where do we go from here? Deans generally ignore arguments that we should just hire more faculty to replace losses, largely because when faculty retire or leave, that’s the only point at which a university can re-think its priorities. Furthermore, some of our arguments for a bigger software engineering group at U of T went away. Our department withdrew the specialist degree in software engineering, and reduced the number of SE undergrad courses, largely because we didn’t have the faculty to teach them, and finding qualified sessional instructors was always a struggle. In effect, our department has gradually walked away from having a strong software engineering group, due to resource constraints.

I believe very firmly that our department *does* need a strong software engineering group, for a number of reasons. First, it’s an important part of an undergrad CS education. The majority of our students go on to work in the software industry, and for this, it is vital that they have a thorough understanding of the engineering principles of software construction. Many of our competitors in N America run majors and/or specialist programs in software engineering, to feed the enormous demand from the software industry for more graduates. One could argue that this should be left to the engineering schools, but these schools tend to lack sufficient expertise in discrete math and computing theory. I believe that software engineering is rooted intellectually in computer science and that a strong software engineering program needs the participation (and probably the leadership) of a strong computer science department. This argument suggests we should be re-building the strength in software engineering that we used to have in our undergrad program, rather than quietly letting it whither.

Secondly, the complexity of modern software systems makes software engineering research ever more relevant to society. Our ability to invent new software technology continues to outpace our ability to understand the principles by which that software can be made safe and reliable. Software companies regularly come to us seeking to partner with us in joint research and to engage with our grad students. Currently, we have to walk away from most of these opportunities. That means research funding we’re missing out on.

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)

• Zero dimensional Energy Balance from Wolfram. Allows you to adjust one parameter, the greenhouse effect, and explore the resulting equilibrium global temperature. Also serves to show off Wolfram’s Computable Document Format (CDF) which might be a neat way to share simple models with students.
• A simple spreadsheet zero-dimension energy balance model from Climateprediction.net. I like the idea of getting the students to do this in spreadsheets, because most of them already understand spreadsheets. This one has a parameter for heat capacity, so you can see how long it takes to reach a new equilibrium temperature.
• Energy Balance Model with ability to add atmosphere layers from Phet. This simulation shows off how greenhouse gas molecules interact with photons, trapping them or allowing them through. Pretty neat for giving students an intuition for how the greenhouse effect works.
• One-dimensional Energy Balance model from Shodor. Calculates the equilibrium temperature for each latitude zone on the planet, allowing you to specify cloud and ice albedo, solar constant, longwave radiation loss, and starting temperatures for each zone. Not very usable, but good illustration of what a 1-dimensional model might do. (Note: Shodor also have a great ecosystem sim with rabbits and wolves, and a disease transmission sim).
• A one-layer energy-balance model developed by Michael Mann at Penn state, for use in his course on global warming. Allows you to alter different feedback factors (albedo, clouds, ice, water vapour), to test their effect on temperature and climate sensitivity.
• A Java applet Global Energy Balance Model, from Rob MacKay at Clark College (part of a whole collection of Java Physlets from Davidson University)
• The Global Equilibrium Energy Balance Interactive Tinker Toy (Geebitt) from Chris Petersen at NASA GISS, a more sophisticated spreadsheet model.
• An energy flow simulator written in NetLogo from Northwestern U, showing how heat transfer works between the sun, atmosphere and ground, and allowing you to adjust CO2 and cloudiness dynamically.

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

• Isaac Held on how simple models are like fruit flies in biological research – we can use them to give us a first test of whether an idea makes sense.
• John Baez has been collecting basic introductions to the different types of climate model, and has an entire grad course that walks through the mathematics of climate modelling.

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.

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.

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.
   • Lynch, P. (2008). The origins of computer weather prediction and climate modeling. Journal of Computational Physics, 227(7), 3431-3444.
   • Weart, S. (2010). The development of general circulation models of climate. Studies In History and Philosophy of Science Part B: Studies In History and Philosophy of Modern Physics, 41(3), 208-217.
2. Calculating the weather. Bjerknes’ equations. ENIAC runs. What does a modern dynamical core do? [Includes basic introduction to thermodynamics of atmosphere and ocean]
   • Platzman, G. W. (1979). The ENIAC Computations of 1950: Gateway to Numerical Weather Prediction. Bulletin of the American Meteorological Society, 60, 302-312.
   • Staniforth, a, & Wood, N. (2008). Aspects of the dynamical core of a nonhydrostatic, deep-atmosphere, unified weather and climate-prediction model. Journal of Computational Physics, 227(7), 3445-3464.
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.
   • Müller, P. (2010). Constructing climate knowledge with computer models. Wiley Interdisciplinary Reviews: Climate Change.
   • Weart, S. (2012). Simple Models of Climate Change. The Discovery of Global Warming.
   • Gramelsberger, G. (2010). Conceiving processes in atmospheric models – General equations, subscale parameterizations, and “superparameterizations.” Studies In History and Philosophy of Science Part B: Studies In History and Philosophy of Modern Physics, 41(3), 233-241.
   • Weber, S. L. (2010). The utility of Earth system Models of Intermediate Complexity (EMICs). Wiley Interdisciplinary Reviews: Climate Change, (April).
5. Earth System Modeling. Using models to study interactions in the earth system. Overview of key systems (carbon cycle, hydrology, ice dynamics, biogeochemistry).
   • Dahan, A. (2010). Putting the Earth System in a numerical box? The evolution from climate modeling toward global change. Studies In History and Philosophy of Science Part B: Studies In History and Philosophy of Modern Physics, 41(3), 282-292.
   • Claussen, M. (2007). Climate system models – a brief introduction. Developments in Quaternary Science, 7, 495-497.
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).
   • Washington, W. M., Buja, L., & Craig, A. (2009). The computational future for climate and Earth system models: on the path to petaflop and beyond. Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 367(1890), 833-46.
   • Slingo, J., Bates, K., Nikiforakis, N., Piggott, M., Roberts, M., Shaffrey, L., Stevens, I., et al. (2009). Developing the next-generation climate system models: challenges and achievements. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 367(1890), 815-31.
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.
   • Oreskes, N. (2001). Philosophical Issues in Model Assessment. Model validation: Perspectives in.
   • Oreskes, N., Shrader-Frechette, K., & Belitz, K. (1994). Verification, validation, and confirmation of numerical models in the earth sciences. Science, 263(5147), 641.
   • Knutti, R. (2008). Should we believe model predictions of future climate change? Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 366(1885), 4647-64.
   • Reichler, T., & Kim, J. (2008). How Well Do Coupled Models Simulate Today’s Climate? Bulletin of the American Meteorological Society, 89(3), 303-311.
   • Shackley, S., Young, P., & Parkinson, S. (1998). Uncertainty, complexity and concepts of good science in climate change modelling: are GCMs the best tools? Climatic Change, 38, 159-205.
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?
   • Masson, D., & Knutti, R. (2011). Climate model genealogy. Geophysical Research Letters, 38(8), 1-4.
   • Pennell, C., & Reichler, T. (2011). On the Effective Number of Climate Models. Journal of Climate, 24(9), 2358-2367.
   • Murphy, J. M., Sexton, D. M. H., Barnett, D., & Jones, G. S. (2004). Quantification of modelling uncertainties in a large ensemble of climate change simulations. Nature, 430(August 2004).
   • Hawkins, E., & Sutton, R. (2009). The Potential to Narrow Uncertainty in Regional Climate Predictions. Bulletin of the American Meteorological Society, 90(8), 1095-1107.
   • Hargreaves, J. C. (2010). Skill and uncertainty in climate models. Wiley Interdisciplinary Reviews: Climate Change, 1.
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)
    • Collins, M. (2007). Ensembles and probabilities: a new era in the prediction of climate change. Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 365(1857), 1957-70.
    • Leutbecher, M., & Palmer, T. N. (2008). Ensemble forecasting. Journal of Computational Physics, 227(7), 3515-3539.
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.
    • Taylor, K. E., Stouffer, R. J., & Meehl, G. A. (2011). A Summary of the CMIP5 Experiment Design.
    • Moss, R. H., Edmonds, J. A., Hibbard, K. a, Manning, M. R., Rose, S. K., van Vuuren, D. P., Carter, T. R., et al. (2010). The next generation of scenarios for climate change research and assessment. Nature, 463(7282), 747-56.
    • New, M., Liverman, D., Schroeder, H., Schroder, H., & Anderson, K. (2011). Four degrees and beyond: the potential for a global temperature increase of four degrees and its implications. Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 369(1934), 6-19.
    • Agrawala, S., Broad, K., & Guston, D. H. (2001). Integrating Climate Forecasts and Societal Decision Making: Challenges to an Emergent Boundary Organization. Science, Technology & Human Values, 26(4), 454-477.
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.
    • Ramanathan, V., & Feng, Y. (2008). On avoiding dangerous anthropogenic interference with the climate system: Formidable challenges ahead. Proc. of the Nat. Acad. of Sciences, 105(38), 14245-14250.
    • Stainforth, D. a., Allen, M. R., Tredger, E. R., & Smith, L. a. (2007). Confidence, uncertainty and decision-support relevance in climate predictions. Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 365(1857), 2145-61.
    • Randalls, S. (2010). History of the 2°C climate target. Wiley Interdisciplinary Reviews: Climate Change, 1(4), 598-605.
    • Hansen, J. E., Sato, M., Kharecha, P., Beerling, D. J., Berner, R., Masson-Delmotte, V., Pagani, M., et al. (2008). Target atmospheric CO2: Where should humanity aim? Open Atmospheric Science Journal, 2(15), 217-231.
    • Turner, G. M. (2012). On the Cusp of Global Collapse? Gaia, 21(2), 116-124.
    • Sterman, J. D., & Sweeney, L. B. (2002). Cloudy skies: assessing public understanding of global warming. System Dynamics Review, 18(2), 207-240.

Ray’s talk spanned 120 years of research on climate change. The key message is that science is a long, slow process of discovery, in which theories (and their predictions) tend to emerge long before they can be tested. We often learn just as much from the predictions that turned out to be wrong as we do from those that were right. But successful predictions eventually form the body of knowledge that we can be sure about, not just because they were successful, but because they build up into a coherent explanation of multiple lines of evidence.

Here are the sucessful predictions:

1896: Svante Arrhenius correctly predicts that increases in fossil fuel emissions would cause the earth to warm. At that time, much of the theory of how atmospheric heat transfer works was missing, but nevertheless, he got a lot of the process right. He was right that surface temperature is determined by the balance between incoming solar energy and outgoing infrared radiation, and that the balance that matters is the radiation budget at the top of the atmosphere. He knew that the absorption of infrared radiation was due to CO2 and water vapour, and he also knew that CO2 is a forcing while water vapour is a feedback. He understood the logarithmic relationship between CO2 concentrations in the atmosphere and surface temperature. However, he got a few things wrong too. His attempt to quantify the enhanced greenhouse effect was incorrect, because he worked with a 1-layer model of the atmosphere, which cannot capture the competition between water vapour and CO2, and doesn’t account for the role of convection in determining air temperatures. His calculations were incorrect because he had the wrong absorption characteristics of greenhouse gases. And he thought the problem would be centuries away, because he didn’t imagine an exponential growth in use of fossil fuels.

Arrhenius, as we now know, was way ahead of his time. Nobody really considered his work again for nearly 50 years, a period we might think of as the dark ages of climate science. The story perfectly illustrates Paul Hoffman’s tongue-in-cheek depiction of how scientific discoveries work: someone formulates the theory, other scientists then reject it, ignore it for years, eventually rediscover it, and finally accept it. These “dark ages” weren’t really dark, of course – much good work was done in this period. For example:

• 1900: Frank Very worked out the radiation balance, and hence the temperature, of the moon. His results were confirmed by Pettit and Nicholson in 1930.
• 1902-14: Arthur Schuster and Karl Schwarzschild used a 2-layer radiative-convective model to explain the structure of the sun.
• 1907: Robert Emden realized that a similar radiative-convective model could be applied to planets, and Gerard Kuiper and others applied this to astronomical observations of planetary atmospheres.

This work established the standard radiative-convective model of atmospheric heat transfer. This treats the atmosphere as two layers; in the lower layer, convection is the main heat transport, while in the upper layer, it is radiation. A planet’s outgoing radiation comes from this upper layer. However, up until the early 1930′s, there was no discussion in the literature of the role of carbon dioxide, despite occasional discussion of climate cycles. In 1928, George Simpson published a memoir on atmospheric radiation, which assumed water vapour was the only greenhouse gas, even though, as Richardson pointed out in a comment, there was evidence that even dry air absorbed infrared radiation.

1938: Guy Callendar is the first to link observed rises in CO2 concentrations with observed rises in surface temperatures. But Callendar failed to revive interest in Arrhenius’s work, and made a number of mistakes in things that Arrhenius had gotten right. Callendar’s calculations focused on the radiation balance at the surface, whereas Arrhenius had (correctly) focussed on the balance at the top of the atmosphere. Also, he neglected convective processes, which astrophysicists had already resolved using the radiative-convective model. In the end, Callendar’s work was ignored for another two decades.

1956: Gilbert Plass correctly predicts a depletion of outgoing radiation in the 15 micron band, due to CO2 absorption. This depletion was eventually confirmed by satellite measurements. Plass was one of the first to revisit Arrhenius’s work since Callendar, however his calculations of climate sensitivity to CO2 were also wrong, because, like Callendar, he focussed on the surface radiation budget, rather than the top of the atmosphere.

1961-2: Carl Sagan correctly predicts very thick greenhouse gases in the atmosphere of Venus, as the only way to explain the very high observed temperatures. His calculations showed that greenhouse gasses must absorb around 99.5% of the outgoing surface radiation. The composition of Venus’s atmosphere was confirmed by NASA’s Venus probes in 1967-70.

1959: Burt Bolin and Erik Eriksson correctly predict the exponential increase in CO2 concentrations in the atmosphere as a result of rising fossil fuel use. At that time they did not have good data for atmospheric concentrations prior to 1958, hence their hindcast back to 1900 was wrong, but despite this, their projection for changes forward to 2000 were remarkably good.

1967: Suki Manabe and Dick Wetherald correctly predict that warming in the lower atmosphere would be accompanied by stratospheric cooling. They had built the first completely correct radiative-convective implementation of the standard model applied to Earth, and used it to calculate a +2C equilibrium warming for doubling CO2, including the water vapour feedback, assuming constant relative humidity. The stratospheric cooling was confirmed in 2011 by Gillett et al.

1975: Suki Manabe and Dick Wetherald correctly predict that the surface warming would be much greater in the polar regions, and that there would be some upper troposphere amplification in the tropics. This was the first coupled general circulation model (GCM), with an idealized geography. This model computed changes in humidity, rather than assuming it, as had been the case in earlier models. It showed polar amplification, and some vertical amplification in the tropics. The polar amplification was measured, and confirmed by Serreze et al in 2009. However, the height gradient in the tropics hasn’t yet been confirmed (nor has it yet been falsified – see Thorne 2008 for an analysis)

1989: Ron Stouffer et. al. correctly predict that the land surface will warm more than the ocean surface, and that the southern ocean warming would be temporarily suppressed due to the slower ocean heat uptake. These predictions are correct, although these models failed to predict the strong warming we’ve seen over the antarctic peninsula.

Of course, scientists often get it wrong:

1900: Knut Angström incorrectly predicts that increasing levels of CO2 would have no effect on climate, because he thought the effect was already saturated. His laboratory experiments weren’t accurate enough to detect the actual absorption properties, and even if they were, the vertical structure of the atmosphere would still allow the greenhouse effect to grow as CO2 is added.

1971: Rasool and Schneider incorrectly predict that atmospheric cooling due to aerosols would outweigh the warming from CO2. However, their model had some important weaknesses, and was shown to be wrong by 1975. Rasool and Schneider fixed their model and moved on. Good scientists acknowledge their mistakes.

1993: Richard Lindzen incorrectly predicts that warming will dry the troposphere, according to his theory that a negative water vapour feedback keeps climate sensitivity to CO2 really low. Lindzen’s work attempted to resolve a long standing conundrum in climate science. In 1981, the CLIMAP project reconstructed temperatures at the last Glacial maximum, and showed very little tropical cooling. This was inconsistent the general circulation models (GCMs), which predicted substantial cooling in the tropics (e.g. see Broccoli & Manabe 1987). So everyone thought the models must be wrong. Lindzen attempted to explain the CLIMAP results via a negative water vapour feedback. But then the CLIMAP results started to unravel, and newer proxies demonstrated that it was the CLIMAP data that was wrong, rather than the models. It eventually turns out the models were getting it right, and it was the CLIMAP data and Lindzen’s theories that were wrong. Unfortunately, bad scientists don’t acknowledge their mistakes; Lindzen keeps inventing ever more arcane theories to avoid admitting he was wrong.

1995: John Christy and Roy Spencer incorrectly calculate that the lower troposphere is cooling, rather than warming. Again, this turned out to be wrong, once errors in satellite data were corrected.

In science, it’s okay to be wrong, because exploring why something is wrong usually advances the science. But sometimes, theories are published that are so bad, they are not even wrong:

2007: Courtillot et. al. predicted a connection between cosmic rays and climate change. But they couldn’t even get the sign of the effect consistent across the paper. You can’t falsify a theory that’s incoherent! Scientists label this kind of thing as “Not even wrong”.

Finally, there are, of course, some things that scientists didn’t predict. The most important of these is probably the multi-decadal fluctuations in the warming signal. If you calculate the radiative effect of all greenhouse gases, and the delay due to ocean heating, you still can’t reproduce the flat period in the temperature trend in that was observed in 1950-1970. While this wasn’t predicted, we ought to be able to explain it after the fact. Currently, there are two competing explanations. The first is that the ocean heat uptake itself has decadal fluctuations, although models don’t show this. However, it’s possible that climate sensitivity is at the low end of the likely range (say 2°C per doubling of CO2), it’s possible we’re seeing a decadal fluctuation around a warming signal. The other explanation is that aerosols took some of the warming away from GHGs. This explanation requires a higher value for climate sensitivity (say around 3°C), but with a significant fraction of the warming counteracted by an aerosol cooling effect. If this explanation is correct, it’s a much more frightening world, because it implies much greater warming as CO2 levels continue to increase. The truth is probably somewhere between these two. (See Armour & Roe, 2011 for a discussion)

To conclude, climate scientist have made many predictions about the effect of increasing greenhouse gases that have proven to be correct. They have earned a right to be listened to, but is anyone actually listening? If we fail to act upon the science, will future archaeologists wade through AGU abstracts and try to figure out what went wrong? There are signs of hope – in his re-election acceptance speech, President Obama revived his pledge to take action, saying “We want our children to live in an America that …isn’t threatened by the destructive power of a warming planet.”