A common cry from climate contrarians is that climate models need better verification and validation (V&V), and in particular, that they need Independent V&V (aka IV&V). George Crews has been arguing this for a while, and now Judith Curry has taken up the cry. Having spent part of the 1990′s as lead scientist at NASA’s IV&V facility, and the last few years studying climate model development processes, I think I can offer some good insights into this question.
The short answer is “no, they don’t”. The slightly longer answer is “if you have more money to spend to enhance the quality of climate models, spending it on IV&V is probably the least effective thing you could do”.
The full answer involves deconstructing the question, to show that it is based on three incorrect assumptions about climate models: (1) that there’s some significant risk to society associated with the use of climate models; (2) that the existing models are inadequately tested / verified / validated / whatevered; and (3) that trust in the models can be improved by using an IV&V process. I will demonstrate what’s wrong with each of these assumptions, but first I need to explain what IV&V is.
Independent Verification and Validation (IV&V) is a methodology developed primarily in the aerospace industry for reducing the risk of software failures, by engaging a separate team (separate from the software development team, that is) to perform various kinds of testing and analysis on the software as it is produced. NASA adopted IV&V for development of the flight software for the space shuttle in the 1970′s. Because IV&V is expensive (it typically adds 10%-20% to the cost of a software development contract), NASA tried to cancel the IV&V on the shuttle in the early 1980′s, once the shuttle was declared operational. Then, of course the Challenger disaster occurred. Although software wasn’t implicated, a consequence of the investigation was the creation of the Leveson committee, to review the software risk. Leveson’s committee concluded that far from cancelling IV&V, NASA needed to adopt the practice across all of its space flight programs. As a result of the Leveson report, the NASA IV&V facility was established in the early 1990′s, as a centre of expertise for all of NASA’s IV&V contracts. In 1995, I was recruited as lead scientist at the facility, and while I was there, our team investigated the operational effectiveness of the IV&V contracts on the Space Shuttle, International Space Station, Earth Observation System, Cassini, as well as a few other smaller programs. (I also reviewed the software failures on NASA’s Mars missions in the 1990′s, and have a talk about the lessons learned)
The key idea for IV&V is that when NASA puts out a contract to develop flight control software, it also creates a separate contract with a different company, to provide an ongoing assessment of software quality and risk as the development proceeds. One difficulty with IV&V contracts in the US aerospace industry is that it’s hard to achieve real independence, because industry consolidation has left very few aerospace companies available to take on such contracts, and they’re not sufficiently independent from one another.
NASA’s approach demands independence along three dimensions:
- managerial independence (the IV&V contractor is free to determine how to proceed, and where to devote effort, independently of either the software development contractor and the customer)
- financial independence (the funding for the IV&V contract is separate from the development contract, and cannot be raided if more resources are needed for development); and
- technical independence (the IV&V contractor is free to develop its own criteria, and apply whatever V&V methods and tools it deems appropriate).
This has led to the development of a number of small companies who specialize only in IV&V (thus avoiding any contractual relationship with other aerospace companies), and who tend to recruit ex-NASA staff to provide them with the necessary domain expertise.
For the aerospace industry, IV&V has been demonstrated to be a cost effective strategy to improve software quality and reduce risk. The problem is that the risks are extreme: software errors in the control software for a spacecraft or an aircraft are highly likely to cause loss of life, loss of the vehicle, and/or loss of the mission. There is a sharp distinction between the development phase and the operation phase for such software: it had better be correct when it’s launched. Which means the risk mitigation has to be done during development, rather than during operation. In other words, iterative/agile approaches don’t work – you can’t launch with a beta version of the software. The goal is to detect and remove software defects before the software is ever used in an operational setting. An extreme example of this was the construction of the space station, where the only full end-to-end construction of the system was done in orbit; it wasn’t possible to put the hardware together on the ground in order to do a full systems test on the software.
IV&V is essential for such projects, because it overcomes natural confirmation bias of software development teams. Even the NASA program managers overseeing the contracts suffer from this too – we discovered one case where IV&V reports on serious risks were being systematically ignored by the NASA program office, because the program managers preferred to believe the project was going well. We fixed this by changing the reporting structure, and routing the IV&V reports directly to the Office of Safety and Mission Assurance at NASA headquarters. The IV&V teams developed their own emergency strategy too – if they encountered a risk that they considered mission-critical, and couldn’t get the attention of the program office to address it, they would go and have a quiet word with the astronauts, who would then ensure the problem got seen to!
But IV&V is very hard to do right, because much of it is a sociological problem rather than a technical problem. The two companies (developer and IV&V contractor) are naturally set up in an adversarial relationship, but if they act as adversaries, they cannot be effective: the developer will have a tendency to hide things, and the IV&V contractor will have a tendency to exaggerate the risks. Hence, we observed that the relationship is most effective where there is a good horizontal communication channel between the technical staff in each company, and that they come to respect one another’s expertise. The IV&V contractor has to be careful not to swamp the communication channels with spurious low-level worries, and the development contractor must be willing to respond positively to criticism. One way this works very well is for the IV&V team to give the developers advance warning of any issues they planned to report up the hierarchy to NASA, so that the development contractor could have a solution in place as even before NASA asked for it. For a more detailed account of these coordination and communication issues, see:
Okay, let’s look at whether IV&V is applicable to climate modeling. Earlier, I identified three assumptions made by people advocating it. Let’s take them one at a time:
1) The assumption there’s some significant risk to society associated with the use of climate models.
A large part of the mistake here is to misconstrue the role of climate models in policymaking. Contrarians tend to start from an assumption that proposed climate change mitigation policies (especially any attempt to regulate emissions) will wreck the economies of the developed nations (or specifically the US economy, if it’s an American contrarian). I prefer to think that a massive investment in carbon-neutral technologies will be a huge boon to the world’s economy, but let’s set aside that debate, and assume for sake of arguments that whatever policy path the world takes, it’s incredibly risky, with a non-neglibable probability of global catastrophe if the policies are either too aggressive or not aggressive enough, i.e. if the scientific assessments are wrong.
The key observation is that software does not play the same role in this system that flight software does for a spacecraft. For a spacecraft, the software represents a single point of failure. An error in the control software can immediately cause a disaster. But climate models are not control systems, and they do not determine climate policy. They don’t even control it indirectly – policy is set by a laborious process of political manoeuvring and international negotiation, in which the impact of any particular climate model is negligible.
Here’s what happens: the IPCC committees propose a whole series of experiments for the climate modelling labs around the world to perform, as part of a Coupled Model Intercomparison Project. Each participating lab chooses those runs they are most able to do, given their resources. When they have completed their runs, they submit the data to a public data repository. Scientists around the world then have about a year to analyze this data, interpret the results, to compare performance of the models, discuss findings at conferences and workshops, and publish papers. This results in thousands of publications from across a number of different scientific disciplines. The publications that make use of model outputs take their place alongside other forms of evidence, including observational studies, studies of paleoclimate data, and so on. The IPCC reports are an assessment of the sum total of the evidence; the model results from many runs of many different models are just one part of that evidence. Jim Hansen rates models as the third most important source of evidence for understanding climate change, after (1) paleoclimate studies and (2) observed global changes.
The consequences of software errors in a model, in the worst case, are likely to extend to no more than a few published papers being retracted. This is a crucial point: climate scientists don’t blindly publish model outputs as truth; they use model outputs to explore assumptions and test theories, and then publish papers describing the balance of evidence. Further papers then come along that add more evidence, or contradict the earlier findings. The assessment reports then weigh up all these sources of evidence.
I’ve been asking around for a couple of years for examples of published papers that were subsequently invalidated by software errors in the models. I’ve found several cases where a version of the model used in the experiments reported in a published paper was later found to contain an important software bug. But in none of those cases did the bug actually invalidate the conclusions of the paper. So even this risk is probably overstated.
The other point to make is that around twenty different labs around the world participate in the Model Intercomparison Projects that provide data for the IPCC assessments. That’s a level of software redundancy that is simply impossible in the aerospace industry. It’s likely that these 20+ models are not quite as independent as they might be (e.g. see Knutti’s analysis of this), but even so, the ability to run many different models on the same set of experiments, and to compare and discuss their differences is really quite remarkable, and the Model Intercomparison Projects have been a major factor in driving the science forward in the last decade or so. It’s effectively a huge benchmarking effort for climate models, with all the benefits normally associated with software benchmarking (and worthy of a separate post – stay tuned).
So in summary, while there are huge risks to society of getting climate policy wrong, those risks are not software risks. A single error in the flight software for a spacecraft could kill the crew. A single error in a climate model can, at most, only affect a handful of the thousands of published papers on which the IPCC assessments are based. The actual results of a particular model run are far less important than the understanding the scientists gain about what the model is doing and why, and the nature of the uncertainties involved. The modellers know that the models are imperfect approximations of very complex physical, chemical and biological processes. Conclusions about key issues such as climate sensitivity are based not on particular model runs, but on many different experiments with many different models over many years, and the extent to which these experiments agree or disagree with other sources of evidence.
2) the assumption that the current models are inadequately tested / verified / validated / whatevered;
This is a common talking point among contrarians. Part of the problem is that while the modeling labs have evolved sophisticated processes for developing and testing their models, they rarely bother to describe these processes to outsiders – nearly all published reports focus on the science done with the models, rather than the modeling process itself. I’ve been working to correct this, with, first, my study of the model development processes at the UK Met Office, and more recently my comparative studies of other labs, and my accounts of the existing V&V processes. Some people have interpreted the latter as a proposal for what should be done, but it is not; it is an account of the practices currently in place across all the of the labs I have studied.
A key point is that for climate models, unlike spacecraft flight controllers, there is no enforced separation between software development and software operation. A climate model is always an evolving, experimental tool, it’s never a finished product – even the prognostic runs done as input to the IPCC process are just experiments, requiring careful interpretation before any conclusions can be drawn. If the model crashes, or gives crazy results, the only damage is wasted time.
This means that an iterative development approach is the norm, which is far superior to the waterfall process used in the aerospace industry. Climate modeling labs have elevated the iterative development process to a new height: each change to the model is treated as a scientific experiment, where the change represents a hypothesis for how to improve the model, and a series of experiments is used to test whether the hypothesis was correct. This means that software development proceeds far more slowly than commercial software practices (at least in terms of lines of code per day), but that the models are continually tested and challenged by the people who know them inside out, and comparison with observational data is a daily activity.
The result is that climate models have very few bugs, compared to commercial software, when measured using industry standard defect density measures. However, although defect density is a standard IV&V metric, it’s probably a poor measure for this type of software – it’s handy for assessing risk of failure in a control system, but a poor way of assessing the validity and utility of a climate model. The real risk is that there may be latent errors in the model that mean it isn’t doing what the modellers designed it to do. The good news is that such errors are extremely rare: nearly all coding defects cause problems that are immediately obvious: the model crashes, or the simulation becomes unstable. Coding defects can only remain hidden if they have an effect that is small enough that it doesn’t cause significant perturbations in any of the diagnostic variables collected during a model run; in this case they are indistinguishable from the acceptable imperfections that arise as a result of using approximate techniques. The testing processes for the climate models (which in most labs include a daily build and automated test across all reference configurations) are sufficient that such problems are nearly always identified relatively early.
This means that there are really only two serious error types that can lead to misleading scientific results: (1) misunderstanding of what the model is actually doing by the scientists who conduct the model experiments, and (2) structural errors, where specific earth system processes are omitted or poorly captured in the model. In flight control software, these would correspond to requirements errors, and would be probed by an IV&V team through specification analysis. Catching these in control software is vital because you only get one chance to get it right. But in climate science, these are science errors, and are handled very well by the scientific process: making such mistakes, learning from them, and correcting them are all crucial parts of doing science. The normal scientific peer review process handles these kinds of errors very well. Model developers publish the details of their numerical algorithms and parameterization schemes, and these are reviewed and discussed in the community. In many cases, different labs will attempt to build their own implementations from these descriptions, and in the process subject them to critical scrutiny. In other words, there is already an independent expert review process for the most critical parts of the models, using the normal scientific route of replicating one another’s techniques. Similarly, experimental results are published, and the data is made available for other scientists to explore.
As a measure of how well this process works for building scientifically valid models, one senior modeller recently pointed out to me that it’s increasingly the case now that when the models diverge from the observations, it’s often the observational data that turns out to be wrong. The observational data is itself error prone, and software models turn out to be an important weapon in identifying and eliminating such errors.
However, there is another risk here that needs to be dealt with. Outside of the labs where the models are developed, there is a tendency for scientists who want to make use of the models to treat them as black box oracles. Proper use of the models depends on a detailed understanding of their strengths and weaknesses, and the ways in which uncertainties are handled. If we have some funding available to improve the quality of climate models, it would be far better spent on improving the user interfaces, and better training of the broader community of model users.
The bottom line is that climate models are subjected to very intensive system testing, and the incremental development process incorporates a sophisticated regression test process that’s superior to most industrial software practices. The biggest threat to validity of climate models is errors in the scientific theories on which they are based, but such errors are best investigated through the scientific process, rather than through an IV&V process. Which brings us to:
(3) the assumption that our ability to trust in the models can be improved by an IV&V process;
IV&V is essentially a risk management strategy for safety-critical software when which an iterative development strategy is not possible – where the software has to work correctly the first (and every) time it is used in an operational setting. Climate models aren’t like this at all. They aren’t safety critical, they can be used even while they are being developed (and hence are built by iterative refinement); and they solve complex, wicked problems, for which there’s no clear correctness criteria. In fact, as a species of software development process, I’ve come to the conclusion they are dramatically different from any of the commercial software development paradigms that have been described in the literature.
A common mistake in the software engineering community is to think that software processes can be successfully transplanted from one organisation to another. Our comparative studies of different software organizations show that this is simply not true, even for organisations developing similar types of software. There are few, if any, documented cases of a software development organisation successfully adopting a process model developed elsewhere, without very substantial tailoring. What usually happens is that ideas from elsewhere are gradually infused and re-fashioned to work in the local context. And the evidence shows that every software oganisation evolves its own development processes that are highly dependent on local context, and on the constraints they operate under. Far more important than a prescribed process is the development of a shared understanding within the software team. The idea of taking a process model that was developed in the aerospace industry, and transplanting it wholesale into a vastly different kind of software development process (climate modeling) is quite simply ludicrous.
For example, one consequence of applying IV&V is that it reduces flexibility for development team, as they have to set clearer milestones and deliver workpackages on schedule (otherwise IV&V team cannot plan their efforts). Because the development of scientific codes is inherently unpredictable, would be almost impossible to plan and resource an IV&V effort. The flexibility to explore new model improvements opportunistically, and to adjust schedules to match varying scientific rhythms, is crucial to the scientific mission – locking the development into more rigid schedules to permit IV&V would be a disaster.
If you wanted to set up an IV&V process for climate models, it would have to be done by domain experts; domain expertise is the single most important factor in successful use of IV&V in the aerospace industry. This means it would have to be done by other climate scientists. But other climate scientists already do this routinely – it’s built into the Model Intercomparison Projects, as well as the peer review process and through attempts to replicate one another’s results. In fact the Model Intercomparison Projects already achieve far more than an IV&V process would, because they are done in the open and involve a much broader community.
In other words, the available pool of talent for performing IV&V is already busy using a process that’s far more effective than IV&V ever can be: it’s called doing science. Actually, I suspect that those people calling for IV&V of climate models are really trying to say that climate scientists can’t be trusted to check each other’s work, and that some other (unspecified) group ought to do the IV&V for them. However, this argument can only be used by people who don’t understand what IV&V is. IV&V works in the aerospace industry not because of any particular process, but because it brings in the experts – the people with grey hair who understand the flight systems inside out, and understand all the risks.
And remember that IV&V is expensive. NASA’s rule of thumb was an additional 10%-20% of the development cost. This cannot be taken from the development budget – it’s strictly an additional cost. Given my estimate of the development cost of a climate model as somewhere in the ballpark of $350 million, then we’ll need to find another $35 million for each climate modeling centre to fund their IV&V contract. And if we had such funds to add to their budgets, I would argue that IV&V is one of the least sensible ways of spending this money. Instead, I would:
- Hire more permanent software support staff to work alongside the scientists;
- Provide more training courses to give the scientists better software skills;
- Do more research into modeling frameworks;
- Experiment with incremental improvements to existing practices, such as greater use of testing tools and frameworks, pair programming and code sprints;
- More support to grow the user communities (e.g. user workshops and training courses), and more community building and beta testing;
- Documenting the existing software development and V&V best practices so that different labs can share ideas and experiences, and the process of model building becomes more transparent to outsiders.
To summarize, IV&V would be an expensive mistake for climate modeling. It would divert precious resources (experts) away from existing modeling teams, and reduce their flexibility to respond to the science. IV&V isn’t appropriate because this isn’t
missionsafety-critical software, it doesn’t have distinct development and operational phases, and the risks of software error are minor. There’s no single point of failure, because many labs around the world build their own models, and the normal scientific processes of experimentation, peer-review, replication, and model inter-comparison already provide a sophisticated process to examine the scientific validity of the models. Virtually all coding errors are detected in routine testing, and science errors are best handled through the usual scientific process, rather than through an IV&V process. Furthermore, there is only a small pool of experts available to perform IV&V on climate models (namely, other climate modelers) and they are already hard at work improving their own models. Re-deploying them to do IV&V of each other’s models would reduce the overall quality of the science rather than improving it.
(BTW I shouldn’t have had to write this article at all…)