One of the questions I’ve been chatting to people about this week at the WCRP Open Science Conference this week is whether climate modelling needs to be reorganized as an operational service, rather than as a scientific activity. The two respond to quite different goals, and hence would be organized very differently:

  • An operational modelling centre would prioritize stability and robustness of the code base, and focus on supporting the needs of (non-scientist) end-users who want models and model results.
  • A scientific modelling centre focusses on supporting scientists themselves as users. The key priority here is to support the scientists’ need to get their latest ideas into the code, to run experiments and get data ready to support publication of new results. (This is what most climate modeling centres do right now).

Both need good software practices, but those practices would look very different in the case when the scientists are building code for their own experiments, versus serving the needs of other communities. There are also very different resource implications: an operational centre that serves the needs of a much more diverse set of stakeholders would need a much larger engineering support team in relation to the scientific team.

The question seems very relevant to the conference this week, as one of the running themes has been the question of what “climate services” might look like. Many of the speakers call for “actionable science”, and there has been a lot of discussion of how scientists should work with various communities who need knowledge about climate to inform their decision-making.

And there’s clearly a gap here, with lots of criticism of how it works at the moment. For example, here’s a great from Bruce Hewitson on the current state of climate information:

“A proliferation of portals and data sets, developed with mixed motivations, with poorly articulated uncertainties and weakly explained assumptions and dependencies, the data implied as information, displayed through confusing materials, hard to find or access, written in opaque language, and communicated by interface organizations only semi‐aware of the nuances, to a user community poorly equipped to understand the information limitations”

I can’t argue with any of that. But it begs the question as to whether solving this problem requires a reconceptualization of climate modeling activities to make them much more like operational weather forecasting centres?

Most of the people I spoke to this week think that’s the wrong paradigm. In weather forecasting, the numerical models play a central role, and become the workhorse for service provision. The models are run every day, to supply all sorts of different types of forecasts to a variety of stakeholders. Sure, a weather forecasting service also needs to provide expertise to interpret model runs (and of course, also needs a vast data collection infrastructure to feed the models with observations). But in all of this, the models are absolutely central.

In contrast, for climate services, the models are unlikely to play such a central role. Take for example, the century-long runs, such as those used in the IPCC assessments. One might think that these model runs represent an “operational service” provided to the IPCC as an external customer. But this is a fundamentally mistaken view of what the IPCC is and what it does. The IPCC is really just the scientific community itself, reviewing and assessing the current state of the science. The CMIP5 model runs currently being done in preparation for the next IPCC assessment report, AR5, are conducted by, and for, the science community itself. Hence, these runs have to come from science labs working at the cutting edge of earth system modelling. An operational centre one step removed from the leading science would not be able to provide what the IPCC needs.

One can criticize the IPCC for not doing enough to translate the scientific knowledge into something that’s “actionable” for different communities that need such knowledge. But that criticism isn’t really about the modeling effort (e.g. the CMIP5 runs) that contributes to the Working Group 1 reports. It’s about how the implications of the working group 1 translate into useful information in working groups 2 and 3.

The stakeholders who need climate services won’t be interested in century-long runs. At most they’re interest in decadal forecasts (a task that is itself still in it’s infancy, and a long way from being ready for operational forecasting). More often, they will want help interpreting observational data and trends, and assessing impacts on health, infrastructure, ecosystems, agriculture, water, etc. While such services might make use of data from climate model runs, it generally involve run models regularly in an operational mode. Instead the needs would be more focussed on downscaling the outputs from existing model run datasets. And sitting somewhere between current weather forecasting and long term climate projections is the need for seasonal forecasts and regional analysis of trends, attribution of extreme events, and so on.

So I don’t think it makes sense for climate modelling labs to move towards an operational modelling capability. Climate modeling centres will continue to focus primarily on developing models for use within the scientific community itself. Organizations that provide climate services might need to develop their own modelling capability, focussed more on high resolution, short term (decadal or shorter) regional modelling, and of course, on assessment models that explore the interaction of socio-economic factors and policy choices. Such assessment models would make use of basic climate data from global circulation models (for example, calculations of climate sensitivity, and spatial distributions of temperature change), but don’t connect directly with climate modeling.

This week, I presented our poster on Benchmarking and Assessment of Homogenisation Algorithms for the International Surface Temperature Initiative (ISTI) at the WCRP Open Science Conference (click on the poster for a readable version).

This work is part of the International Surface Temperature Initiative (ISTI) that I blogged about last year. The intent is to create a new open access database for historial surface temperature records at a much higher resolution than has previously been available. In the past, only monthly averages were widely available; daily and sub-daily observations collected by meteorological services around the world are often considered commercially valuable, and hence tend to be hard to obtain. And if you go back far enough, much of the data was never digitized and some is held in deteriorating archives.

The goal of the benchmarking part of the project is to assess the effectiveness of the tools used to remove data errors from the raw temperature records. My interest in this part of the project stems from the work that my student, Susan Sim, did a few years ago on the role of benchmarking to advance research in software engineering. Susan’s PhD thesis described a theory that explains why benchmarking efforts tend to accelerate progress within a research community. The main idea is that creating a benchmark brings the community together to build consensus on what the key research problem is, what sample tasks are appropriate to show progress, and what metrics should be used to measure that progress. The benchmark then embodies this consensus, allowing different research groups to do detailed comparisons of their techniques, and facilitating sharing of approaches that work well.

Of course, it’s not all roses. Developing a benchmark in the first place is hard, and requires participation from across the community; a benchmark put forward by a single research group is unlikely to accepted as unbiased by other groups. This also means that a research community has to be sufficiently mature in terms of their collaborative relationships and consensus on common research problems (in Kuhnian terms, they must be in the normal science phase). Also, note that a benchmark is anchored to a particular stage of the research, as it captures problems that are currently challenging; continued use of a benchmark after a few years can lead to a degeneration of the research, with groups over-fitting to the benchmark, rather than moving on to harder challenges. Hence, it’s important to retire a benchmark every few years and replace it with a new one.

The benchmarks we’re exploring for the ISTI project are intended to evaluate homogenization algorithms. These algorithms detect and remove artifacts in the data that are due to things that have nothing to do with climate – for example when instruments designed to collect short-term weather data don’t give consistent results over the long-term record. The technical term for these is inhomogeneities, but I’ll try to avoid the word, not least because I find it hard to say. I’d like to call them anomalies, but that word is already used in this field to mean differences in temperature due to climate change. Which means that anomalies and inhomogeneities are, in some ways, opposites: anomalies are the long term warming signal that we’re trying to assess, and inhomogeneities represent data noise that we have to get rid of first. I think I’ll just call them bad data.

Bad data arise for a number of reasons, usually isolated to changes at individual recording stations: a change of instruments, an instrument drifting out of calibration, a re-siting, a slow encroachment of urbanization which changes the local micro-climate. Because these problems tend to be localized, they can often be detected by statistical algorithms that compare individual stations with their neighbours. In essence, the algorithms look for step changes and spurious trends in the data such as the following:

These bad data are a serious problem in climate science – for a recent example, see the post yesterday at RealClimate, which discusses how homogenization algorithms might have gotten in the way of understanding the relationship between climate change and the Russian heatwave of 2010. Unhelpfully, they’re also used by deniers to beat up climate scientists, as some people latched onto the idea of blaming warming trends on bad data rather than, say, actual warming. Of course, this ignores two facts: (1) climate scientists already spend a lot of time assessing and removing such bad data and (2) independent analysis has repeatedly shown that the global warming signal is robust with respect to such data problems.

However, such problems in the data still matter for the detailed regional assessments that we’ll need in the near future for identifying vulnerabilities (e.g. to extreme weather), and, as the example at RealClimate shows, for attribution studies for localized weather events and hence for decision-making on local and regional adaptation to climate change.

The challenge is that it’s hard to test how well homogenization algorithms work, because we don’t have access to the truth – the actual temperatures that the observational records should have recorded. The ISTI benchmarking project aims to fill this gap by creating a data set that has been seeded with artificial errors. The approach reminds me of the software engineering technique of bug seeding (aka mutation testing), which deliberately introduce errors into software to assess how good the test suite is at detecting them.

The first challenge is where to get a “clean” temperature record to start with, because the assessment is much easier if the only bad data in the sample are the ones we deliberately seeded. The technique we’re exploring is to start with the output of a Global Climate Model (GCM), which is probably the closest we can get to a globally consistent temperature record. The GCM output is on a regular grid, and may not always match the observational temperature record in terms of means and variances. So to make it as realistic as possible, we have to downscale the gridded data to yield a set of “station records” that match the location of real observational stations, and adjust the means and variances to match the real-world:

Then we inject the errors. Of course, the error profile we use is based on what we currently know about typical kinds of bad data in surface temperature records. It’s always possible there are other types of error in the raw data that we don’t yet know about; that’s one of the reasons for planning to retire the benchmark periodically and replace it with a new one – it allows new findings about error profiles to be incorporated.

Once the benchmark is created, it will be used within the community to assess different homogenization algorithms. Initially, the actual injected error profile will be kept secret, to ensure the assessment is honest. Towards the end of the 3-year benchmarking cycle, we will release the details about the injected errors, to allow different research groups to measure how well they did. Details of the results will then be included in the ISTI dataset for any data products that use the homogenization algorithms, so that users of these data products have more accurate estimates of uncertainty in the temperature record. Such estimates are important, because use of the processed data without a quantification of uncertainty can lead to misleading or incorrect research.

For more details of the project, see the Benchmarking and Assessment Working Group website, and the group blog.

How would you like to help the weather and climate research community digitize historical records before they’re lost forever to a fate such as this:

Watch this video, from the International Surface Temperature Initiative‘s data rescue initiative for more background (skip to around 2:20 for the interesting parts):

…and then get involved with the Data Rescue at Home Projects:

Our specialissue of IEEE Software, for Nov/Dec 2011, is out! The title for the issue is Climate Change: Science and Software, and the guest editors were me, Paul Edward, Balaji, and Reinhard Budich.

There’s a great editorial by Forrest Shull, reflecting on interviews he conducted with Robert Jacob at Argonne National Labs and Gavin Schmidt at NASA GISS. The papers in the issue are:

Unfortunately most of the content is behind a paywall, although you can read our guest editors introduction in full here. I’m working on making some of the other content more freely available too.

This is really last week’s news, but I practice slow science. A new web magazine has launched:

The aim is to cover more in-depth analysis of climate change & sustainability, to get away from the usual false dichotomy between “deniers” and “activists”, and more into the question of what kind of future we’d like, and how we get there. Constructive, science-based discussions are welcome, and will be moderated to ensure the discussion threads are worth reading. The site also features a “best of the blogs” feed, and we’re experimenting with models for open peer-review for the more in-depth articles. And as an experimental collaborative project, there’s an ongoing discussion on how to build a community portal.

I’m proud to serve on the scientific review panel, and delighted that my essay on leverage points has been the main featured article this week.

Go check out the site and join in the discussions!

I spent some time yesterday exploring the Occupy Toronto happening in St James’ Park, and snapping photos. There’s something wonderful about the atmosphere there – one that makes me more optimistic for the future:

The place was a hive of activity. One of the things that impressed me the most was the organization that’s gone into it. There’s a medical tent (with a sign requesting no photos), a media tent (with free wifi):

an open library with an extensive set of books:

a makeshift kitchen:

…and the all important whiteboard listing what’s needed:

It was my first time seeing the human mic in action, and I was blown away by how that works as a community-building exercise.

And definitely kid-friendly:

And unlike most political rallies that feature celebrity speakers, this is entirely a grassroots affair. There’s some people down there who are brilliant at building the quiet, open atmosphere that’s conducive to community building.

And plenty of fun to be had doing sign writing:

Some of the signs had a strong Canadian theme. Our mayor (Rob Ford) features in many of them – for the non-locals, we have a ridiculously over the top right-wing mayor who tried to slash spending for everything from libraries to daycare this summer, and got a serious comeuppance from the citizenry in response. That’s him, on the orange sign below, being mocked for his election slogan that he would clear out the “gravy” from city hall (his consultants’ report showed the city is run very efficiently, and there is no gravy!). Oh, look, and right next to it, a Climate Justice sign:

Interestingly, the signs focus on sustainability just as much as economics. Here’s on that combines both themes very nicely:

And of course, plenty of artistic talent:

What unites both the climate crisis and the financial crisis? What is it that has driven scientists and environmentalists to risk arrest in protests across the world? What is it that’s driven people from all walks of life to show up in their thousands to occupy their cities? In both cases, there’s a growing sense that the system is fundamentally broken, and that our current political elites are unable (rather than just unwilling) to fix them. And in both cases, it’s becoming increasingly apparent that our current political system is a major cause of the problems. Which therefore makes it even harder to discover solutions.

So how do we make progress? If we’re going to take seriously the problems that have led people to take to the streets, then we have to understand the processes that are steadily driving us in the wrong direction when it comes to things we care about – a clean environment, a stable climate, secure jobs, a stable economy. In other words, we have to understand the underlying systems, understand the dynamics within those systems, and we have to find the right leverage points that would allow us to change those dynamics to work the way we would like.

Failure to take a systems view is evident throughout discussions of climate change, and now, more recently, throughout mainstream media discussions about the Occupy protests. Suggestions for what needs fixing tend to focus on superficial aspects of the systems that matter, mainly by tinkering with parameters (emissions targets, stabilization wedges, the size of the debt, the bank interest rate, etc). If the system itself is broken, you can’t fix it by adjusting its current parameters – you have to look at the underlying dynamics and change the structure of the system that gave rise to the problem in the first place. Most people are focusing on the wrong leverage points. Even worse, in some cases, they are pushing in the wrong direction on some of the leverage points…

Perhaps the best analysis of this I’ve ever seen is Donella Meadows’ essay on leverage points. [If you’re not already familiar with it, I highly recommend reading it before tackling the rest of this post]. Meadows has written some wonderfully accessible material on systems thinking, but only gives a very brief overview in this particular essay, because she’s focussing here on how to identify leverage points that allow one to alter a system. She identifies twelve places to look, and orders them, roughly, from the least effective to the most effective.

To illustrate the point, I’ll begin with a much simpler system than the ones we really want to fix. My example is the controllability of water temperature in a shower. The particular shower I have in mind is in a small hotel in Paris, and is a little antiquated, the result of old-fashioned plumbing. It takes time for the hot water to reach the shower head from the hot water tank, and there’s enough of a delay between the taps that control the hot and cold water and the temperature response, that you’re forever trying to adjust it to get a good temperature. It’s too cold, so you turn up the hot tap. The temperature barely seems to change, so you crank it up a lot. After a few minutes the water heats up so much it’s scalding. So you crank up the cold tap. Again, the temperature responds slowly until you realise it’s now too cold. You turn down the cold tap, and soon find it’s too hot again. And so on.

Does this remind you of the economy? Or, for that matter, the way the physical climate system works over the course of tens of thousands of years? More worryingly, it’s the outcome I expect if we ever try to geo-engineer our way out of extreme climate change. Right now, the human race is cranking up the hot tap. But the system responds very slowly. And by the time we’ve realized the heat has built up, we’ll have overshot our comfort zones. We’ll slam on the brakes and end up overcompensating. Because the system is just as hard to control (actually, a damn sight harder!) than that annoying shower in Paris.

Let’s look at how Meadows’ twelve leverage points might help us analyze the Parisian shower. At #12, we have what is usually the least effective place to seek change:

#12 Changes in constants, parameters, numbers. Example: Change the set point on the water tank thermostat. In general, such adjustments make no difference to the controllability of the shower. [There is an interesting exception, when we’re prepared to make really big adjustments. For example, if we crank the thermostat on the hot water tank all the way down to ‘pleasantly warm’ we’ll never have to balance the hot and cold taps again, we can just use the “hot” tap. Usually, such really big adjustments are unlikely to be made, for other reasons].

#11. Change the sizes of buffers and stocks relative to their flows. Example: Get a bigger hot water tank. This will make the energy bills bigger, but still won’t make the shower any more controllable.

#10. Change the structure of material stocks and flows. Example: Replace the water pipes with smaller diameter pipes. This might help, as it reduces the thermal mass of the pipes, and hence, may affect the lag between the water tank and the shower, leading to more responsive shower controls.

#9. Change the length of delays, relative to rate of system change. Example: Relocate the hot water tank closer to the bathroom; or Wait a little longer for temperature response to settle before touching the taps again. Such changes might be hard to achieve (hence they’re high up on the list), but very effective if we could do them.

#8. Increase the strength of negative feedback loops relative to the impacts they try to correct against. Example: Take a deep breath and calm down – you’re the negative feedback trying to keep the system stable. If you’re less impulsive on the taps, you’ll help to dampen the temperature fluctuations. If the system is changing too quickly, or is subject to instability, identifying the negative feedbacks, and working to strengthen them can often yield simple and effective leverage points. But when you’re in the shower getting scalded, it might be hard to remember this.

#7. Reduce the gain around positive feedback loops. Example: Replace the taps with ones that offer a finer level of control. This reduces the big temperature fluctuations when we turn the taps too quickly, and hence reduces the positive feedback loop that leads to temperature overshoot.

#6. Change the structure of information flows, to alter who does (or does not) have access to information. Example: Put an adjustable marker on the shower dial to record a preferred setting. Changing the flows of information about a system is generally much easier and cheaper than changing any other aspect of a system, hence, it’s often a more powerful leverage point than any of the above. For the shower, this one tiny fix may entirely cure the temperature fluctuation problem.

#5. Change the rules of the system (incentives, punishments, constraints). Example: Set limits on amount of time you can shower for. This might reduce the incentive to spend time fiddling with the temperature controls. But who will enforce the constraint?

#4. Nurture the power to add, change, evolve or self-organize system structure. Example: Teach yourself to tolerate a wider range of shower temperatures. or: Design a new automated temperature controller.

#3. Change the goal of the system. Example: Focus on getting clean quickly rather than getting the water to exactly the desired temperature. Of course, changing the goal of the system is hard, because it means changing people’s perceptions of the system itself.

#2. Change the mindset or paradigm out of which the system arises. Example: Is cleanliness over-rated? or: why stay in these antiquated hotels in Paris anyway? Paradigm shifts are hard to achieve, but when they happen, they have a dramatic transformative effect on the systems that arose in the previous paradigm.

#1. The power to transcend paradigms. Example: Learn systems thinking and gain the ability to understand a system from multiple perspectives; Realise that system structure and behaviour arises from the dominant paradigm; Explore how our own perspectives shape our interactions with the system.

Note that Meadows emphasizes the point that all twelve types of leverage point can be effective for changing systems, if you have a good understanding of how the system works, and can make good choices for where to make changes. However, in a self-perpectuating system, the dynamics that created the problem you’re trying to solve will also tend to defeat most kinds of change, unless they really do alter those dynamics in an important way.

Note that for many of these examples, I’ve chosen to include the person in the shower as part of my ‘shower system’. More importantly, some of my suggestions refer to how the person in the shower understands the shower system, and how her understanding of the system affects the system’s behaviour. This is to emphasize a mistake we often make when thinking about both the climate and the economy. In both cases, we have to understand the role that people play within these systems, and especially how our expectations and cultural norms themselves form part of the system. If people, in general, have the wrong mental model of how the systems work, it’s significantly more challenging to figure out how to fix things when they go wrong.

Let’s look at how the list of leverage points applies to the climate system and the financial system.

#12 Changes in Constants, parameters, numbers.

  • Climate System: tighten pollution standards, negotiate stronger version of the Kyoto protocol, increase fuel taxes, etc.
  • Financial System: change the interest base rates, increase size of stimulus spending, increase taxes, cut government spending, put caps on campaign contributions, increase the minimum wage, vote for the other party.

While changing the parameters of the existing system can make a difference, it’s rare that it does. Systems tend to operate in regions where small parameter adjustments make no difference to the overall stability of the system. If there’s a systemic effect that is pushing a system in the wrong direction (dependence on fossil fuels, financial instability, poverty, etc), then adjusting the system’s parameters is unlikely to make much difference, if you don’t also change the structure of the system.

None of these examples are likely to make much difference to the underlying problems. To understand this point, you have to understand the system you’re dealing with. For example, the whole problem of climate change itself might appear to be the result of a small parameter change – a small increase in radiative forcing, caused by a small increase (measured in parts per million!) in atmospheric concentrations of certain gases. But that’s not the real cause. The real cause is a systemic change in human activity that traces back to the industrial revolution: a new source of energy was harnessed, which then kicked off mutually reinforcing positive feedback loops in human population growth and energy use. A few more parts per million of CO2 in the atmosphere is not the problem; the problem is a new exponential trend that did not exist previously.

However, remember there’s sometimes an exception, if you make very large adjustments. For the climate system, you could increase fuel taxes so that gas (petrol) costs, say, ten times as much as it does today. Such an adjustment would be guaranteed to change the system (but not perhaps, when the mobs are done with you, in the way you intended). Here’s an interesting rule of thumb: if you change any parameter in a system by an order of magnitude or more, what you get is an entirely different type of system. Try it: a twenty-storey building is fundamentally different from a 2-storey house. A ten-lane freeway is fundamentally different from a single lane road. A salary of $1 million is fundamentally different from a salary of $100K.

#11. Change the sizes of buffers and stocks relative to their flows

  • Climate System: Plant more forests to create bigger carbon sinks. Ocean fertilization and/or artificial trees to soak up carbon, etc.
  • Financial System: Increase the federal reserve, require banks to hold larger reserves, increase debt ceiling limits.

In many systems these are hard to change, as they require large investments in infrastructure (the canonical example is a large dam to create a buffer in the water supply). This also means it can be hard to make frequent, fine-grained adjustments. More importantly, they tie up resources – keeping a large stock means that the stock isn’t working for you: your bigger water tank will make your energy bills much higher (and won’t affect your shower adjustment problem anyway). Making banks keep larger reserves will make them much less dynamic, and will reduce the funds available for lending.

All of these things might ease the problem a little, but none of them will make any significant difference to the cause of the problem. No matter how big you make the reserves, you’ll quickly be defeated by the exponential growth curves that you didn’t tackle.

#10. Change the structure of material stocks and flows

  • Climate System: Carbon Capture and Storage (diverts emissions at the point they are generated, so they don’t enter the atmosphere).
  • Financial System: Create a Tobin tax, which diverts a small percentage of each financial transaction to create a new pool of money to fix problems. Create new kinds of super-tax on the very rich. Separate the high street banks from their gambling investment operations.

Physical structure is also, usually, very hard to change, once the system is operating, although it’s sometimes easier to change how things flow than it is to create new buffers. However, both types of change tend to have limited impact, because the stocks and flows arise from the nature of the system – which means the system itself will find ways of defeating your efforts, for the same reason it ended up like it is now.

For example, separating high street banks from investment firms won’t really achieve much. People will find other ways to gamble bank money on foolish investments, if you haven’t actually addressed the reasons why such investments are made in the first place. Similarly for carbon capture and storage — diverting some percentage of the carbon that would go into the atmosphere via (expensive) CCS won’t help if our use of fossil fuels continues to grow at the rate it has done in the past. The fundamental problem of exponential growth in fossil fuel use will always outstrip our attempts to sequester some of it. There’s also the problem that on the timescales that matter (decades to centuries), it’s not clear the carbon will stay put. Oh, and CCS is only ever likely to be feasible on large, static sites like big power plants, so won’t make any different to emissions from transport, aviation, agriculture, etc.

#9. Change the length of delays, relative to rate of system change

  • Climate System: Speed up widescale deployment of clean energy technologies. Speed up the legislative process for climate policy. Speed up implementation of new standards for emissions. Use a faster ramp up on carbon pricing. Lengthen the approval process for new fossil fuel plants, oil pipelines, etc.
  • Financial System: Lengthen the approval process for risky loans, mergers, etc. Speed up implementation of government jobs programs. Slow down economic growth (to remove the boom and bust cycles).

Often, these kinds of change can be very powerful leverage points, but many of the delays in large systems are impossible to shorten – things take as long as they take. Also, as Meadows points out, most people try to shift things in the wrong direction, as many of these changes are counter-intuitive. For example, reducing the delay in money transfer times just increases chance of wild gyrations in the markets. Governments around the world usually seek to maximize economic growth, when often they should be trying to dampen it.

#8. Increase the strength of negative feedback loops relative to the impacts they try to correct against.

  • Climate System: Make the price of all goods reflect their true environmental cost. Remove perverse subsidies to fossil fuel companies (the cost of extraction & processing should be a negative feedback loop on dependence on fossil fuels); introduce better monitoring and data collection for global carbon fluxs, to more quickly assess impacts of different actions.
  • Financial System: More transparent democracy to allow people to vote out corrupt politicians more quickly; Remove subsidies, bailouts, etc (these distort the negative feedbacks that keep the financial system stable); protection for whistleblowers; more scrutiny of boardroom pay rises by unions and shareholders.

Negative feedbacks are what tend to keep a system stable. If the system is changing too quickly, or is subject to instability, identifying the negative feedbacks, and working to strengthen them, can often yield simple and effective leverage points. All of the examples here a likely to be more effective at fixing the respective systems than anything we’ve mentioned so far. Some of them rely on people acting as negative feedbacks. For example, by offering better protection for whistleblowers, you create a culture in which the people are less likely yield to corrupting influences.

#7. Reduce the gain around positive feedback loops

  • Climate System: Higher energy efficiency standards (this dampens the growth in energy demand); Green development – mechanisms that allow people to improve their quality of life without needing to increase their use of fossil fuels; wider use of birth control to curb population increases.
  • Financial System: Punish bankers who make reckless investment decisions (discourages others from following suit). Use a more progressive tax structure and introduce very high inheritance taxes (these prevent the rich from getting ever richer). High quality & free public education (to prevent the rich from forming privileged elites). Forgive all student loans on graduation (ends the cycle of individual indebtedness)
  • Both Systems: Slower economic growth (above, I described this as a “length of delay” issue – as slower growth can allow other processes, such as clean energy technology to keep up; but more importantly, economic growth is itself a positive feedback loop that drives ever more resource consumption, financial fluctuations and environmental degradation.

Runaway feedback loops inevitably destroy a system, unless some negative feedback loops kick in. In the long run, a negative feedback always kicks in: we use up all the resources, we kill off most of the population, we bankrupt everyone. But by that time the system has already been destroyed. The trick is to dampen the positive feedbacks long before too much damage is done, and this is often much more effective than trying to boost the strength of countervailing negative feedbacks. For example, if we want to address inequality, using tax structures that stop the rich getting ever richer is much more effective than creating anti-poverty programs aimed at mopping up the resulting inequalities.

Economic growth is an important example here. Remember that economic growth is a measure of the change in GDP over time. And GDP itself is a measure of the volume of financial transactions, or in other words, how fast money is flowing through the system. Accelerating these money flows makes all the instabilities in the financial system much worse. Worse still, one of the primary ways that GDP grows is by ever accelerating consumption of resources, so you get a self-reinforcing positive feedback loop between over-consumption and economic growth.

#6. Change the structure of information flows, to alter who does (or does not) have access to information

  • Climate System: Get IPCC assessment results out to the public faster, and in more accessible formats. Put journalists in touch with climate scientists. Label products with lifecycle carbon footprint data. Put meters in cars showing the total cost of each journey.
  • Financial System: Publish pay and benefit rates for all employees of private companies. Publish details of all political donations. Increase government oversight of financial transactions.
  • Both Systems: Open access to data, e.g. on campaign financing, carbon emissions, etc; Require all lobbyists and think tanks to publish full details on funding sources.

Changing the flows of information about a system is generally much easier and cheaper than changing any other aspect of a system, which means these can be very powerful leverage points. Many of these examples are powerful enough to cause significant changes to the underlying behaviour in the system, because they expose problems to the people that shape the behaviour of the system.

Providing people with full information about the true cost of things at the point they use them is a very powerful inducement to change behaviour. Meadows gives an example of electricity meters in the front hall of a home, rather than in the basement. Another one that bugs me is the information imbalance between different transportation choices. We always know how exactly how much a trip will cost by public transit, but the cost of driving is largely invisible (paying to fill up, paying the insurance and maintenance bills, etc are too far removed from the actual per-journey decisions). Some potential fixes are very simple: Google maps could show the cost, as well as the distance & time, when doing journey planning.

#5. Change the rules of the system (incentives, punishments, constraints)

  • Climate System: Government grants for energy efficiency projects. Free public transit. Jail-time for executives whose companies break carbon emissions rules. Mandatory science comprehension testing for anyone standing for public office.
  • Financial System: Give workers the right to vote on boardroom pay rates. New regulations on what banks can and cannot do with investors’ money. Remove immunity from prosecution for politicians. Ban all private funding of political campaigns.
  • Both Systems: Strict limits on ownership of media outlets. New ethics rules for journalists and advertisers.

These changes tend to impact the behaviour of the system immediately (as long as they’re actually enforced), and hence can have very high leverage. Unfortunately, one of the problems with fixing both the climate and financial systems is that our systems for changing the rules (e.g. legislative processes) are themselves broken. Large corporations (especially fossil fuel companies) have, over a period of many years, deliberately co-opted legislative processes to meet their own goals. Where once it might have been possible for governments to pass new laws to address climate change, or to change the way governments allocate resources, now they cannot, because these processes no longer act in the interest of the people. Similarly, the mainstream media has been co-opted by the same vested interests, so that people are fed little more than propaganda about how great the system is.

#4. Nurture the power to add, change, evolve or self-organize system structure

  • Climate System: Evidence-based policymaking. Resilient communities such as Transition Towns (these empower individual communities to manage their own process of ending dependence on fossil fuels).
  • Financial System: Switch from private companies to credit unions and worker-owned cooperatives.
  • Both Systems: Change to proportional representation for elections (this gives a more diverse set of political parties access to power, and helps voters feel their vote counts). Celebrate social diversity and give greater access to political power for minorities (this removes the tendency to have one dominant culture, and hence helps build resilience).

Systems gain the ability to evolve because of diversity within them. In biology, we understand this well – the diversity of an ecosystem is essential for evolutionary processes to work. Unfortunately, few people understand this matters just as much for social systems. Societies with a single dominant culture tend to find it very hard to change, while societies that encourage and promote diversity are also laying the foundations for new ideas and new forms of self-organization to emerge.

The political culture in the US is case in point. US politics is dominated by two parties that share an almost identical set of cultural assumptions, especially to do with the capitalist system, the role of markets, and the correct way to manage the economy. This makes the US particularly resistant to change, no matter how much the evidence accumulates that the system isn’t working.

#3. Change the goal of the system

  • Climate System: Don’t focus on emission reduction, focus on eliminating each and every dependency on fossil fuels. Don’t focus on international negotiations towards a treaty, focus on zero-carbon strategies for each city or region.
  • Financial System: Stop chasing short-term corporate profits as the primary goal of the economy. Corporations should aim for sustainability rather than growth. Instead of measuring GDP, measure gross national happiness.
  • Both Systems: Recognize and challenge the hidden goals of the current system, such as: the tendency for large corporations to maximize their power and influence over national governments; the desire to control access to information via media conglomerates; the desire of ruling elites to perpetuate their control.

Of course, changing the goal of the system is hard, because it means changing people’s perceptions of the system itself. It requires people to be able to step outside the system and see it from a fresh perspective, to identify how the dominant goals of the system shape its structure and operation. In short, it requires people to be systems thinkers.

There’s one kind of goal change related to the climate system that troubles me: instead of trying to prevent climate change, we could instead focus on survival strategies for living on a hotter planet. Given what we understand of the impacts on food, water and habitable regions, this would only be possible for a much smaller human population, and so it entails giving up trying to save as many people as possible. But it’s a very simple leverage point. The problem is that there are both ethical and practical reasons to reject this approach. The ethical reasons are well understood. The practical problem is that humans are very effective at fighting like crazy over diminishing resources, so it’s hard to see how this approach would work in the face of growing waves of climate refugees.

#2. Change the mindset or paradigm out of which the system arises

  • Climate and Financial Systems: Triple-bottom line accounting (forces companies to balance social and environmental impact with profitability). A shift in mindset from consumerism to living in harmony with the environment. A shift from material wealth as a measure of success to (say) social connectedness. A shift in mindset from individualism to community. A shift from individual greed to egalitarianism.

Paradigm shifts are hard to achieve, but when they happen, they transform the systems that arose in the previous paradigm. Much of the root cause of current problems with climate change and financial instability are due to the dominant paradigm of the last thirty years: blind faith in the free market to fix everything, along with accumulation of wealth and material assets as a virtue.

#1. The power to transcend paradigms

  • Climate and Financial Systems: Learn systems thinking and gain the ability to understand a system from multiple perspectives; Realise that system structure and behaviour arises from dominant paradigm. Explore how our own perspectives shape our interactions with the system…
  • … And then take to the streets.

Postscript: Notice that as we proceed down the list, and look at more fundamental changes to the systems, the solutions for climate change and the financial crisis start to merge. Also notice that in both cases, many of my examples aren’t what climate scientists or economists generally talk about. We need to broaden the conversation.

We’ve just announced a special issue of the Open Access Journal Geoscientific Model Development (GMD):

Call for Papers: Special Issue on Community software to support the delivery of CMIP5

CMIP5 represents the most ambitious and computer-intensive model inter-comparison project ever attempted. Integrating a new generation of Earth system models and sharing the model results with a broad community has brought with it many significant technical challenges, along with new community-wide efforts to provide the necessary software infrastructure. This special issue will focus on the software that supports the scientific enterprise for CMIP5, including: couplers and coupling frameworks for Earth system models; the Common Information Model and Controlled Vocabulary for describing models and data; The development of the Earth System Grid Federation; the development of new portals for providing data access to different end-user communities; the scholarly publishing of datasets, and studies of the software development and testing processes used for the CMIP5 models. We especially welcome papers that offer comparative studies of the software approaches taken by different groups, and lessons learnt from community efforts to create shareable software components and frameworks.

See here for submission instructions. The call is open ended, as we can keep adding papers to the special issue. We’ve solicited papers from some of the software projects involved in CMIP5, but welcome unsolicited submissions too.

GMD operates an open review process, whereby submitted papers are posted to the open discussion site (known as GMDD), so that both the invited reviewers and anyone else can make comments on the papers and then discuss such comments with the authors, prior to a final acceptance decision for the journal. I was appointed to the editorial board earlier this year, and am currently getting my first taste of how this works – I’m looking forward to applying this idea to our special issue.

We have funding for a 2-day symposium in the spring of 2012 on the topic “Sustainable Cities in a Post-Carbon World”. Here’s my current blurb for the event – I’m still tweaking the wording, so constructive comments are welcome (and watch this space for more details on date & venue, etc)…

Cities house half the world’s population, consume more than two-thirds of the world’s energy, and produce more than 70% of the global CO2 emissions. The triple threat of climate change, peak oil, and ecosystem loss poses a massive challenge to cities, as they depend on huge inputs of energy, food, and materials from the surrounding regions. Cities are also particularly vulnerable to the effects of climate change, as urban landscapes amplify extreme heat events, with potentially disastrous impact on public health and urban infrastructure.

Modern cities grew up in an era of cheap fossil fuels. As that era ends, our cities can only be sustained if they can make a rapid transition to energy efficiency and renewable fuels, and build greater resilience into their urban infrastructure. Such a transition will mean re-thinking almost every aspect of city life: buildings, lifestyle, transportation, public spaces, water and waste management, energy efficiency, social justice and participatory decision-making. The technologies that will be needed, in general, already exist. But the social and financial structures do not. The path by which rapid, wide scale deployment can occur is unclear: cities grew not so much by design, but through emergence as complex organisms. The hardest questions are not so much what a sustainable city might look like, but how we get there.

The goal of this two-day symposium is to explore new ways to bring together governments, universities and civil society to accelerate this transition to post-carbon cities. With Toronto as a model, we aim to build an action plan to engage the university with city government, NGOs, and community groups, to leverage the inter-disciplinary expertise from across campus to address this challenge.

The symposium will include a mix of talks, panel sessions, workshops, student posters, hands-on activities and movie screenings. Topics will include clean energy, smart grid, transport, urban planning, policy making, city governance, community building, data analytics, public health and green growth/economic development.

Our objectives are to develop new trans-disciplinary thinking for the transition to a post-carbon world; to build new collaborations; and to better integrate current research with urban policy, leading to solutions for sustainable urban development.