Serendipity

At the beginning of March, I was invited to give a talk at TEDxUofT. Colleagues tell me the hardest part of giving these talks is deciding what to talk about. I decided to see if I could answer the question of whether we can trust climate models. It was a fascinating and nerve-wracking experience, quite unlike any talk I’ve given before. Of course, I’d love to do another one, as I now know more about what works and what doesn’t.

Here’s the video and a transcript of my talk. [The bits in square brackets in are things I intended to say but forgot!] 

Computing the Climate: How Can a Computer Model Forecast the Future? TEDxUofT, March 1, 2014.

Talking about the weather forecast is a great way to start a friendly conversation. The weather forecast matters to us. It tells us what to wear in the morning; it tells us what to pack for a trip. We also know that weather forecasts can sometimes be wrong, but we’d be foolish to ignore them when they tell us a major storm is heading our way.

[Unfortunately, talking about climate forecasts is often a great way to end a friendly conversation!] Climate models tell us that by the end of this century, if we carry on burning fossil fuels at the rate we have been doing, and we carry on cutting down forests at the rate we have been doing, the planet will warm by somewhere between 5 to 6 degrees centigrade. That might not seem much, but, to put it into context, in the entire history of human civilization, the average temperature of the planet has not varied by more than 1 degree. So that forecast tells us something major is coming, and we probably ought to pay attention to it.

But on the other hand, we know that weather forecasts don’t work so well the longer into the future we peer. Tomorrow’s forecast is usually pretty accurate. Three day and five day forecasts are reasonably good. But next week? They always change their minds before next week comes. So how can we peer 100 years into the future and look at what is coming with respect to the climate? Should we trust those forecasts? Should we trust the climate models that provide them to us?

Six years ago, I set out to find out. I’m a professor of computer science. I study how large teams of software developers can put together complex pieces of software. I’ve worked with NASA, studying how NASA builds the flight software for the Space Shuttle and the International Space Station. I’ve worked with large companies like Microsoft and IBM. My work focusses not so much on software errors, but on the reasons why people make those errors, and how programmers then figure out they’ve made an error, and how they know how to fix it.

To start my study, I visited four major climate modelling labs around the world: in the UK, in Paris, Hamburg, Germany and in Colorado. Each of these labs have typically somewhere between 50-100 scientists who are contributing code to their climate models. And although I only visited four of these labs, there are another twenty or so around the world, all doing similar things. They run these models on some of the fastest supercomputers in the world, and many of the models have been in construction, the same model, for more than 20 years.

When I started this study, I asked one of my students to attempt to measure how many bugs there are in a typical climate model. We know from our experience with software there are always bugs. Sooner or later the machine crashes. So how buggy are climate models? More specifically, what we set out to measure is what we call “defect density” – How many errors are there per thousand lines of code. By this measure, it turns out climate models are remarkably high quality. In fact, they’re better than almost any commercial software that’s ever been studied. They’re about the same level of quality as the Space Shuttle flight software. Here’s my results (For the actual results you’ll have to read the paper):

We know it’s very hard to build a large complex piece of software without making mistakes.  Even the space shuttle’s software had errors in it. So the question is not “is the software perfect for predicting the future?”. The question is “Is it good enough?” Is it fit for purpose?

To answer that question, we’d better understand what that purpose of a climate model is. First of all, I’d better be clear what a climate model is not. A climate model is not a projection of trends we’ve seen in the past extrapolated into the future. If you did that, you’d be wrong, because you haven’t accounted for what actually causes the climate to change, and so the trend might not continue. They are also not decision-support tools. A climate model cannot tell us what to do about climate change. It cannot tell us whether we should be building more solar panels, or wind farms. It can’t tell us whether we should have a carbon tax. It can’t tell us what we ought to put into an international treaty.

What it does do is tell us how the physics of planet earth work, and what the consequences are of changing things, within that physics. I could describe it as “computational fluid dynamics on a rotating sphere”. But computational fluid dynamics is complex.

I went into my son’s fourth grade class recently, and I started to explain what a climate model is, and the first question they asked me was “is it like Minecraft?”. Well, that’s not a bad place to start. If you’re not familiar with Minecraft, it divides the world into blocks, and the blocks are made of stuff. They might be made of wood, or metal, or water, or whatever, and you can build things out of them. There’s no gravity in Minecraft, so you can build floating islands and it’s great fun.

Climate models are a bit like that. To build a climate model, you divide the world into a number of blocks. The difference is that in Minecraft, the blocks are made of stuff. In a climate model, the blocks are really blocks of space, through which stuff can flow. At each timestep, the program calculates how much water, or air, or ice is flowing into, or out of, each block, and if so, in which directions? It calculates changes in temperature, density, humidity, and so on. And whether stuff such as dust, salt, and pollutants are passing through or accumulating in each block. We have to account for the sunlight passing down through the block during the day. Some of what’s in each block might filter some of the the incoming sunlight, for example if there are clouds or dust, so some of the sunlight doesn’t get down to the blocks below. There’s also heat escaping upwards through the blocks, and again, some of what is in the block might trap some of that heat — for example clouds and greenhouse gases.

As you can see from this diagram, the blocks can be pretty large. The upper figure shows blocks of 87km on a side. If you want more detail in the model, you have to make the blocks smaller. Some of the fastest climate models today look more like the lower figure:

Ideally, you want to make the blocks as small as possible, but then you have many more blocks to keep track of, and you get to the point where the computer just can’t run fast enough. A typical run of a climate model, to simulate a century’s worth of climate, you might have to wait a couple of weeks on some of the fastest supercomputers for that run to complete. So the speed of the computer limits how small we can make the blocks.

Building models this way is remarkably successful. Here’s video of what a climate model can do today. This simulation shows a year’s worth of weather from a climate model. What you’re seeing is clouds and, in orange, that’s where it’s raining. Compare that to a year’s worth of satellite data for the year 2013. If you put them side by side, you can see many of the same patterns. You can see the westerlies, the winds at the top and bottom of the globe, heading from west to east, and nearer the equator, you can see the trade winds flowing in the opposite direction. If you look very closely, you might even see a pulse over South America, and a similar one over Africa in both the model and the satellite data. That’s the daily cycle as the land warms up in the morning and the moisture evaporates from soils and plants, and then later on in the afternoon as it cools, it turns into rain.

Note that the bottom is an actual year, 2013, while the top, the model simulation is not a real year at all – it’s a typical year. So the two don’t correspond exactly. You won’t get storms forming at the same time, because it’s not designed to be an exact simulation; the climate model is designed to get the patterns right. And by and large, it does. [These patterns aren’t coded into this model. They emerge as a consequences of getting the basic physics of the atmosphere right].

So how do you build a climate model like this? The answer is “very slowly”. It takes a lot of time, and a lot of failure. One of the things that surprised me when I visited these labs is that the scientists don’t build these models to try and predict the future. They build these models to try and understand the past. They know their models are only approximations, and they regularly quote the statistician, George Box, who said “All models are wrong, but some are useful”. What he meant is that any model of the world is only an approximation. You can’t get all the complexity of the real world into a model. But even so, even a simple model is a good way to test your theories about the world.

So the way that modellers work, is they spend their time focussing on places where the model does isn’t quite right. For example, maybe the model isn’t getting the Indian monsoon right. Perhaps it’s getting the amount of rain right, but it’s falling in the wrong place. They then form a hypothesis. They’ll say, I think I can improve the model, because I think this particular process is responsible, and if I improve that process in a particular way, then that should fix the simulation of the monsoon cycle. And then they run a whole series of experiments, comparing the old version of the model, which is getting it wrong, with the new version, to test whether the hypothesis is correct. And if after a series of experiments, they believe their hypothesis is correct, they have to convince the rest of the modelling team that this really is an improvement to the model.

In other words, to build the models, they are doing science. They are developing hypotheses, they are running experiments, and using peer review process to convince their colleagues that what they have done is correct:

Climate modellers also have a few other weapons up their sleeves. Imagine for a moment if Microsoft had 25 competitors around the world, all of whom were attempting to build their own versions of Microsoft Word. Imagine further that every few years, those 25 companies all agreed to run their software on a very complex battery of tests, designed to test all the different conditions under which you might expect a word processor to work. And not only that, but they agree to release all the results of those tests to the public, on the internet, so that anyone who wanted to use any of that software can pore over all the data and find out how well each version did, and decide which version they want to use for their own purposes. Well, that’s what climate modellers do. There is no other software in the world for which there are 25 teams around the world trying to build the same thing, and competing with each other.

Climate modellers also have some other advantages. In some sense, climate modelling is actually easier than weather forecasting. I can show you what I mean by that. Imagine I had a water balloon (actually, you don’t have to imagine – I have one here):

I’m going to throw it at the fifth row. Now, you might want to know who will get wet. You could measure everything about my throw: Will I throw underarm, or overarm? Which way am I facing when I let go of it? How much swing do I put in? If you could measure all of those aspects of my throw, and you understand the physics of how objects move, you could come up with a fairly accurate prediction of who is going to get wet.

That’s like weather forecasting. We have to measure the current conditions as accurately as possible, and then project forward to see what direction it’s moving in:

If I make any small mistakes in measuring my throw, those mistakes will multiply as the balloon travels further. The further I attempt to throw it, the more room there is for inaccuracy in my estimate. That’s like weather forecasting. Any errors in the initial conditions multiply up rapidly, and the current limit appears to be about a week or so. Beyond that, the errors get so big that we just cannot make accurate forecasts.

In contrast, climate models would be more like releasing a balloon into the wind, and predicting where it will go by knowing about the wind patterns. I’ll make some wind here using a fan:

Now that balloon is going to bob about in the wind from the fan. I could go away and come back tomorrow and it will still be doing about the same thing. If the power stays on, I could leave it for a hundred years, and it might still be doing the same thing. I won’t be able to predict exactly where that balloon is going to be at any moment, but I can predict, very reliably, the space in which it will move. I can predict the boundaries of its movement. And if the things that shape those boundaries change, for example by moving the fan, and I know what the factors are that shape those boundaries, I can tell you how the patterns of its movements are going to change – how the boundaries are going to change. So we call that a boundary problem:

The initial conditions are almost irrelevant. It doesn’t matter where the balloon started, what matters is what’s shaping its boundary.

So can these models predict the future? Are they good enough to predict the future? The answer is “yes and no”. We know the models are better at some things than others. They’re better at simulating changes in temperature than they are at simulating changes in rainfall. We also know that each model tends to be stronger in some areas and weaker in others. If you take the average of a whole set of models, you get a much better simulation of how the planet’s climate works than if you look at any individual model on its own. What happens is that the weaknesses in any one model are compensated for by other models that don’t have those weaknesses.

But the results of the models have to be interpreted very carefully, by someone who knows what the models are good at, and what they are not good at – you can’t just take the output of a model and say “that’s how it’s going to be”.

Also, you don’t actually need a computer model to predict climate change. The first predictions of what would happen if we keep on adding carbon dioxide to the atmosphere were produced over 120 years ago. That’s fifty years before the first digital computer was invented. And those predictions were pretty accurate – what has happened over the twentieth century has followed very closely what was predicted all those years ago. Scientists also predicted, for example, that the arctic would warm faster than the equatorial regions, and that’s what happened. They predicted night time temperatures would rise faster than day time temperatures, and that’s what happened.

So in many ways, the models only add detail to what we already know about the climate. They allow scientists to explore “what if” questions. For example, you could ask of a model, what would happen if we stop burning all fossil fuels tomorrow. And the answer from the models is that the temperature of the planet will stay at whatever temperature it was when you stopped. For example, if we wait twenty years, and then stopped, we’re stuck with whatever temperature we’re at for tens of thousands of years. You could ask a model what happens if we dig up all known reserves of fossil fuels, and burn them all at once, in one big party? Well, it gets very hot.

More interestingly, you could ask what if we tried blocking some of the incoming sunlight to cool the planet down, to compensate for some of the warming we’re getting from adding greenhouse gases to the atmosphere? There have been a number of very serious proposals to do that. There are some who say we should float giant space mirrors. That might be hard, but a simpler way of doing it is to put dust up in the stratosphere, and that blocks some of the incoming sunlight. It turns out that if you do that, you can very reliably bring the average temperature of the planet back down to whatever level you want, just by adjusting the amount of the dust. Unfortunately, some parts of the planet cool too much, and others not at all. The crops don’t grow so well, and everyone’s weather gets messed up. So it seems like that could be a solution, but when you study the model results in detail, there are too many problems.

Remember that we know fairly well what will happen to the climate if we keep adding CO2, even without using a computer model, and the computer models just add detail to what we already know. If the models are wrong, they could be wrong in either direction. They might under-estimate the warming just as much as they might over-estimate it. If you look at how well the models can simulate the past few decades, especially the last decade, you’ll see some of both. For example, the models have under-estimated how fast the arctic sea ice has melted. The models have underestimated how fast the sea levels have risen over the last decade. On the other hand, they over-estimated the rate of warming at the surface of the planet. But they underestimated the rate of warming in the deep oceans, so some of the warming ends up in a different place from where the models predicted. So they can under-estimate just as much as they can over-estimate. [The less certain we are about the results from the models, the bigger the risk that the warming might be much worse than we think.]

So when you see a graph like this, which comes from the latest IPCC report that just came out last month, it doesn’t tell us what to do about climate change, it just tells us the consequences of what we might choose to do. Remember, humans aren’t represented in the models at all, except in terms of us producing greenhouse gases and adding them to the atmosphere.

If we keep on increasing our use of fossil fuels — finding more oil, building more pipelines, digging up more coal, we’ll follow the top path. And that takes us to a planet that by the end of this century, is somewhere between 4 and 6 degrees warmer, and it keeps on getting warmer over the next few centuries. On the other hand, the bottom path, in dark blue, shows what would happen if, year after year from now onwards, we use less fossil fuels than we did the previous year, until about mid-century, when we get down to zero emissions, and we invent some way to start removing that carbon dioxide from the atmosphere before the end of the century, to stay below 2 degrees of warming.

The models don’t tell us which of these paths we should follow. They just tell us that if this is what we do, here’s what the climate will do in response. You could say that what the models do is take all the data and all the knowledge we have about the climate system and how it works, and put them into one neat package, and its our job to take that knowledge and turn it into wisdom. And to decide which future we would like.

Last week, Damon Matthews from Concordia visited, and gave a guest CGCS lecture, “Cumulative Carbon and the Climate Mitigation Challenge”. The key idea he addressed in his talk is the question of “committed warming” – i.e. how much warming are we “owed” because of carbon emissions in the past (irrespective of what we do with emissions in the future). But before I get into the content of Damon’s talk, here’s a little background.

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.

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)

• 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.

Definitely worth 300 second of your time:

I like this style of video. It reminds me of some other approaches to short explanatory videos I’ve seen recently:

• The Story of Stuff
• The RSA Animate Series
• CommonCraft

William Connolly has written a detailed critique of our paper “Engineering the Software for Understanding Climate Change”, which follows on from a very interesting discussion about “Amateurish Supercomputing Codes?” in his previous post. One of the issues raised in that discussion is the reward structure in scientific labs for software engineers versus scientists. The funding in such labs is pretty much all devoted to “doing science” which invariably means publishable climate science research. People who devote time and effort to improving the engineering of the model code might get a pat on the back, but inevitably it’s under-rewarded because it doesn’t lead directly to publishable science. The net result is that all the labs I’ve visited so far (UK Met Office, NCAR, MPI-M) have too few software engineers working on the model code.

Which brings up another point. Even if these labs decided to devote more budget to the software engineering effort (and it’s not clear how easy it would be to do this, without re-educating funding agencies), where will they recruit the necessary talent? They could try bringing in software professionals who don’t yet have the domain expertise in climate science, and see what happens. I can’t see this working out well on a large scale. The more I work with climate scientists, the more I appreciate how much domain expertise it takes to understand the science requirements, and to develop climate code. The potential culture clash is huge: software professionals (especially seasoned ones) tend to be very opinionated about “the right way to build software”, and insensitive to contextual factors that might make their previous experiences inapplicable. I envision lots of the requirements that scientists care about most (e.g. the scientific validity of the models) getting trampled on in the process of “fixing” the engineering processes. Right now the trade-off between getting the science right versus having beautifully engineered models is tipped firmly in favour of the former. Tipping it the other way might be a huge mistake for scientific progress, and very few people seem to understand how to get both right simultaneously.

The only realistic alternative is to invest in training scientists to become good software developers. Greg Wilson is pretty much the only person around who is covering this need, but his software carpentry course is desperately underfunded. We’re going to need a lot more like this to fix things…

Last week, I ran a workshop for high school kids from across Toronto on “What can computer models tell us about climate change?“. I already posted some of the material I used: the history of our knowledge about climate change. Jorge, Jon and Val ran another workshop after mine, entitled “Climate change and the call to action: How you can make a difference“. They have already blogged their reflections: See Jon’s summary of the workshop plan, and reflections on how to do it better next time, and the need for better metaphors. I think both workshops could have done with being longer, for more discussion and reflection (we were scheduled only 75 minutes for each). But I enjoyed both workshops a lot, as I find it very useful for my own thinking to consider how to talk about climate change with kids, in this case mainly from grade 10 (≈15 years old).

The main idea I wanted to get across in my workshop was the role of computer models: what they are, and how we can use them to test out hypotheses about how the climate works. I really wanted to do some live experiments, but of course, this is a little hard when a typical climate simulation run takes weeks of processing time on a supercomputer. There are some tools that high school kids could play with in the classroom, but none of them are particularly easy to use, and of course, they all sacrifice resolution for ability to run on a desktop machine. Here are some that I’ve played with:

• EdGCM – This is the most powerful of the bunch. It’s a full General Circulation Model (GCM), based on the NASA’s models, and does support many different types of experiment. The license isn’t cheap (personally, I think it ought to be free and open source, but I guess they need a rich sponsor for that), but I’ve been playing with the free 30-day license. A full century of simulation tied up my laptop for 24 hours, but I kinda liked that, as it’s a bit like how you have to wait for results on a full scale model too (it even got hot, and I had to think about how to cool it, again just like a real supercomputer…). I do like the way that the documentation guides you through the process of creating an experiment, and the idea of then ‘publishing’ the results of your experiment to a community website.
• JCM – This is (as far as I can tell) a box model, that allows you to experiment with outcomes of various emissions scenarios, based on the IPCC projections, which means it’s simple enough to give interactive outputs. It’s free and open source, but a little cumbersome to use – the interface doesn’t offer enough guidance for novice users. It might work well in a workshop, with lots of structured guidance for how to use it, but I’m not convinced such a simplistic model offers much value over just showing some of the IPCC graphs and talking about them.
• Climate Interactive (and the C-Roads model). C-ROADS is also a box model, but with the emissions of different countries/regions separated out, to allow exploration of the choices in international climate negotiations. I’ve played a little with C-ROADS, and found it frustrating because it ignores all the physics, and after all, my main goal in playing with climate models with kids is to explore how climate processes work, rather than the much narrower task of analyzing policy choices. It also seems to be hard to tell the difference between various different policy choices – even when I try to run it with  extreme choices (cease all emissions next year vs. business as usual), the outputs are all of a similar shape (“it gets steadily warmer”). This may well be the correct output, but the overall message is a little unfortunate: whatever policy path we choose, the results look pretty similar. Showing the results of different policies as a set of graphs showing the warming response doesn’t seem very insightful; it would be better to explore different regional impacts, but for that we’re back to needing a full GCM.
• CCCSN – the Canadian Climate Change Scenarios Network. This isn’t a model at all, but rather a front end to the IPCC climate simulation dataset. The web tool allows you to get the results from a number of experiments that were run for the IPCC assessments, selecting which model you want, which scenario you want, which output variables you want (temperature, precipitation, etc), and allows you to extract just a particular region, or the full global data. I think this is more useful than C-ROADS, because once you download the data, you can graph it in various ways, and explore how different regions are affected.
• Some Online models collected by David Archer, which I haven’t played with much, but which include some box models, some 1-dimensional models, and the outputs of NCAR’s GCM (which I think is the one of the datasets included in CCCSN). Not much explanation is provided here though – you have to know what you’re doing…
• John Sterman’s Bathtub simulation. Again, a box model (actually, a stocks-and-flows dynamics model), but this one is intended more to educate people about the basic systems dynamics principles, rather than to explore policy choices. So I already like it better than C-ROADS, except that I think the user interface could do with a serious make-over, and there’s way too much explanatory text – there must be a way to do this with more hands on and less exposition. It also suffers from a problem similar t0 C-ROADS: it allows you to control emissions pathways, and explore the result on atmospheric concentrations and hence temperature. But the problem is, we can’t control emissions directly – we have to put in place a set of policies and deploy alternative energy technologies to indirectly affect emissions. So either we’d want to run the model backwards (to ask what emissions pathway we’d have to follow to keep below a specific temperature threshold), or we’d want as inputs the things we can affect – technology deployments, government investment, cap and trade policies, energy efficiency strategies, etc.

None of these support the full range of experiments I’d like to explore in a kids’ workshop, but I think EdGCM is an excellent start, and access to the IPCC datasets via the CCCSN site might be handy. But I don’t like the models that focus just on how different emissions pathways affect global temperature change, because I don’t think these offer any useful learning opportunities about the science and about how scientists work.

I wasn’t going to post anything about the CRU emails story (apart from my attempt at humour), because I think it’s a non-story. I’ve read a few of the emails, and it looks no different to the studies I’ve done of how climate science works. It’s messy. It involves lots of observational data sets, many of which contain errors that have to be corrected. Luckily, we have a process for that – it’s called science. It’s carried out by a very large number of people, any of whom might make mistakes. But it’s self-correcting, because they all review each other’s work, and one of the best ways to get noticed as a scientist is to identify and correct a problem in someone else’s work. There is, of course, a weakness in the way such corrections are usually done to a previously published paper. The corrections appear as letters and other papers in various journals, and don’t connect up well to the original paper. Which means you have to know an area well to understand which papers have stood the test of time and which have been discredited. Outsiders to a particular subfield won’t be able to tell the difference. They’ll also have a tendency to seize on what they think are errors, but actually have already been addressed in the literature.

If you want all the gory details about the CRU emails, read the RealClimate posts (here and here) and the lengthy discussion threads that follow from them. Many of the scientists involved in the CRU emails comment in these threads, and the resulting picture of the complexity of the data and analysis that they have to deal with is very interesting. But of course, none of this will change anyone’s minds. If you’re convinced global warming is a huge conspiracy, you’ll just see scientists trying to circle the wagons and spin the story their way. If you’re convinced that AGW is now accepted as scientific fact, then you’ll see scientists tearing their hair out at the ignorance of their detractors. If you’re not sure about this global warming stuff, you’ll see a lively debate about the details of the science, none of which makes much sense on its own. Don’t venture in without a good map and compass. (Update: I’ve no idea who’s running this site, but it’s a brilliant deconstruction of the allegations about the CRU emails).

But one issue has arisen in many discussions about the CRU emails that touches strongly on the research we’re doing in our group. Many people have looked at the emails and concluded that if the CRU had been fully open with its data and code right from the start, there would be no issue now. This is of course, a central question in Alicia‘s research on open science. While in principle, open science is a great idea, in practice, there are many hurdles, including the fear of being “scooped”, the need to give appropriate credit, the problems of metadata definition and of data provenance, the cost of curation, and the fact that software has a very short shelf-life, and so on. For the CRU dataset at the centre of the current kerfuffle, someone would have to go back to all the data sources, and re-negotiate agreements about how the data can be used. Of course, the anti-science crowd just think that’s an excuse.

However, for climate scientists there is another problem, which is the workload involved in being open. Gavin, at RealClimate, raises this issue in response to a comment that just putting the model code online is not sufficient. For example, if someone wanted to reproduce a graph that appears in a published paper, they’ll need much more: the script files, the data sets, the parameter settings, the spinup files, and so on. Michael Tobis argues that much of this can be solved with good tools and lots of automated scripts. Which would be fine if we were talking about how to help other scientists replicate the results.

Unfortunately, in the special case of climate science, that’s not what we’re talking about. A significant factor in the reluctance of climate scientists to release code and data is to protect themselves from denial-of-service attacks. There is a very well-funded and PR-savvy campaign to discredit climate science. Most scientists just don’t understand how to respond to this. Firing off hundreds of requests to CRU to release data under the freedom of information act, despite each such request being denied for good legal reasons, is the equivalent of frivolous lawsuits. But even worse, once datasets and codes are released, it is very easy for an anti-science campaign to tie the scientists up in knots trying to respond to their attempts to poke holes in the data. If the denialists were engaged in an honest attempt to push the science ahead, this would be fine (although many scientists would still get frustrated – they are human too).

But in reality, the denialists don’t care about the science at all; their aim is a PR campaign to sow doubt in the minds of the general public. In the process, they effect a denial-of-service attack on the scientists – the scientists can’t get on with doing their science because their time is taken up responding to frivolous queries (and criticisms) about specific features of the data. And their failure to respond to each and every such query will be trumpeted as an admission that an alleged error is indeed an error. In such an environment, is it perfectly rational not to release data and code – it’s better to pull up the drawbridge and get on with the drudgery of real science in private. That way the only attacks are complaints about lack of openness. Such complaints are bothersome, but much better than the alternative.

In this case, because the science is vitally important for all of us, it’s actually in the public interest that climate scientists be allowed to withhold their data. Which is really a tragic state of affairs. The forces of anti-science have a lot to answer for.

Update: Robert Grumbine has a superb post this morning on why openness and reproducibility is intrinsically hard in this field