My post on validating climate models suggested that the key validation criteria is the extent to which the model captures (some aspect of) the current scientific theory, and is useful in exploring the theory. In effect, I’m saying that climate models are scientific tools, and should be validated as scientific tools. This makes them very different from, say numerical weather prediction (NWP) software, which are used in an operational setting to provide a service (predicting the weather).

What’s confusing is that both communities (climate modeling and weather modeling) use many of the same techniques both for the design of the models, and for comparing the models with observational data.

For NWP, forecast accuracy is the overriding objective, and the community has developed an extensive methodology for doing forecast verification. I pondered for a while whether this use of the term ‘verification’ here is consistent with my definitions, because surely we should be “validating” a forecast rather than “verifying it”. After thinking about it for a while, I concluded that the terminology is consistent, because forecast verification is like checking a program against it’s specification. In this case the specification states precisely what is being predicted, with what accuracy, and what would constitute a successful forecast (Bob Grumbine gives a recent example in verifying accuracy of seasonal sea ice forecasts). The verification procedure checks that the actual forecast was accurate, within the criteria set by this specification. Whether or not the forecast was useful is another question: that’s the validation question (and it’s a subjective question that requires some investigation of why people want forecasts in the first place).

An important point here is that forecast verification is not software verification: it doesn’t verify a particular piece of software. It’s also not simulation verification: it doesn’t verify a given run produced by that software. It’s verification of an entire forecasting system. A forecasting system makes use of computational models (often more than one), as well as a bunch of experts who interpret the model results.It also includes an extensive data collection system that gathers information about the current state of the world to use as input to the model. (And of course, some forecasting systems don’t use computational models at all). So:

  • If the forecast is inaccurate (according to the forecast criteria), it doesn’t necessarily mean there’s a flaw in the models – it might just as well be a flaw in the interpretation of the model outputs, or in the data collection process that provided it’s inputs. Oh, and of course, the verification might also fail because the specification is wrong, e.g. because there are flaws in the observational system used in the verification procedure too.
  • If the forecasting system persistently produces accurate forecasts (according to the forecast criteria), that doesn’t necessarily tell us anything about the quality of the software itself, it just means that the entire forecast system worked. It may well be that the model is very poor, but the meteorologists who interpret model outputs are brilliant at overcoming the weaknesses in the model (perhaps in the way they configure the runs, or perhaps in the way they filter model outputs), to produce accurate forecasts for their customers.

However, one effect of using this forecast verification approach day-in-day-out for weather forecasting systems over several decades (with an overall demand from customers for steady improvements in forecast accuracy) is that all parts of the forecasting system have improved dramatically over the last few decades, including the software. And climate modelling has benefited from this, as improvements in the modelling of processes needed for NWP can often also be used to improve the climate models (Senior et al have an excellent chapter on this in a forthcoming book, which I will review nearer to the publication date).

The question is, can we apply a similar forecast verification methodology to the “climate forecasting system”, despite the differences between weather and climate?

Note that the question isn’t about whether we can verify the accuracy of climate models this way, because the methodology doesn’t separate the models from the broader system in which they are used. So, if we take this route at all, we’re attempting to verify the forecast accuracy of the whole system: collection of observational data, creation of theories, use of these theories to develop models, choices for which model and which model configuration to use, choices for how to set up the runs, and interpretation of the results.

Climate models are not designed as forecasting tools, they are designed as tools to explore current theories about the climate system, and to investigate sources of uncertainty in these theories. However, the fact that they can be used to project potential future climate change (under various scenarios) is very handy. Of course, this is not the only way to produce quantified estimates of future climate change – you can do it using paper and pencil. It’s also a little unfortunate, because the IPCC process (or at least the end-users of IPCC reports) tend to over-emphasize the model projections at the expense of the science that went into them, and increasingly the funding for the science is tied to the production of such projections.

But some people (both within the climate modeling community and within the denialist community) would prefer that they not be used to project future climate change at all. (The argument from within the modelling community is that the results get over-interpreted or mis-interpreted by lay audiences; the argument from the denialist community is that models aren’t perfect. I think these two arguments are connected…). However, both arguments ignore reality: society demands of climate science that it provides its best estimates of the rate and size of future climate change, and (to the extent that they embody what we currently know about climate) the models are the best tool for this job. Not using them in the IPCC assessments would be like marching into the jungle with one eye closed.

So, back to the question: can we use NWP forecast verification for climate projections? I think the answer is ‘no’, because of the timescales involved. Projections of climate change really only make sense on the scale of decades to centuries. Waiting for decades to do the verification is pointless – by then the science will have moved on, and it will be way too late for policymaking purposes anyway.

If we can’t verify the forecasts on a timescale that’s actually useful, does this mean the models are invalid? Again the answer is ‘no’, for three reasons. First, we have plenty of other V&V techniques to apply to climate models. Second, the argument that climate models are a valid tool for creating future projections of climate change is based not on our ability to do forecast verification, but on how well the models capture the current state of the science. And third, because forecast verification wouldn’t necessarily say anything about the models themselves anyway, as it assesses the entire forecast system.

It would certainly be really, really useful to be able to verify the “climate forecast” system. But the fact that we can’t does not mean we cannot validate climate models.

Should science models be separate from production models?
Validating Climate Models

1 Comment

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  2. I don’t agree with the premise that “For NWP, forecast accuracy is the overriding objective….” Accuracy is only one aspect of the quality of the forecast. As an example, for probabilistic forecasts (either from post-processing or from ensemble approaches), reliable forecasts are typically more important than accurate forecasts. Beyond that, the value of the forecast system is also important. Can users get information from the forecast system? Is it delivered with enough time for users to take action? A highly accurate, but very slow, forecast system is of less value than a less accurate, very fast system. In my experience, the overriding objective for NWP is to have the best (under whatever metric best is defined) forecast system that you can count on to run in the allowed computer time and produce consistent results. For a great discussion of forecast goodness, see

    Murphy, A. H., 1993: What Is a Good Forecast? An Essay on the Nature of Goodness in Weather Forecasting. Wea. Forecasting, 8, 281-293.

    http://journals.ametsoc.org/doi/pdf/10.1175/1520-0434%281993%29008%3C0281%3AWIAGFA%3E2.0.CO%3B2

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