Model Muddle

by | May 1, 2007

A new study suggesting that Arctic ice is melting faster than IPCC forecasts has been reported by the BBC as evidence that the IPCC is too conservative.

Since 1979, the Arctic has been losing summer ice at about 9% per decade, but models on average produce a melting rate less than half that figure… The scientists suggest forecasts from the Intergovernmental Panel on Climate Change (IPCC) may be too cautious.

What the article does not consider is that this is also evidence that IPCC forecasts are not very good. It’s not as if it’s the first time that real-world data has been at odds with the computer models…

This is the third time in the last few months that studies have suggested the IPCC’s latest major global climate analysis, the Fourth Assessment Report, is too conservative.

Here’s a particularly odd section…

This is the opposite view from that put forward by many “climate sceptics”, who view the whole field of computer modelling as deeply flawed

Is the latest real world data not evidence that computer modelling is indeed flawed?

Here’s an odder one…

Because of the way it works, the IPCC is bound to be conservative, as it assesses in considerable depth research already in the public domain. This process takes time, and means the panel’s conclusions will always lag behind the latest publications.

By using ‘conservative’, Richard Black presupposes that newer research will always be painting a bleaker picture than old. Given that hi tech climate models are having such trouble predicting the future, we seriously doubt his own ability to do so.

The failure to account for observational data is not due to the mechanics of the IPCC. It is a problem with the models. To further claim, as Gavin Schmidt of the NASA Goddard Institute for Space Studies (and 1/11th of does, that the problem is that the models are simply ‘insufficiently sensitive’ is also flawed thinking. If the models do not account for observations, then it could be any number of assumptions which were flawed. It does not follow that things are worse than we thought, as Black’s article implies – it merely shows that we were wrong.

Climate modelers and environmentalists want to argue that we can be certain about their predictions, yet when these predictions do not match observations, we are asked to believe that this reflects an even greater degree of certainty. For example, Schmidt says, ‘uncertainty in model projections cuts both ways… My feeling (along with the authors) is that it is likely that the models are insufficiently sensitive.’

It takes a lot of guts to claim one moment that you have the science on your side, and the next that your feelings should be enough to convince the world that you’re right. But gut instinct is no substitute for science.


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