The IPCC model simulation archive

There are lots of ongoing attempts to refine this. What happens if you try and exclude some models that don’t pass an initial screening? Can you weight the models in an optimum way to improve forecasts? Unfortunately, there doesn’t seem to be any universal way to do this despite a few successful attempts. More research on this question is definitely needed.

Note however that the ensemble or meta-ensemble only gives a measure of the central tendency or forced component. They do not help answer the question of whether the models are consistent with any observed change. For that, one needs to look at the spread of the model simulations, noting that each simulation is a potential realisation of the underlying assumptions in the models. Do not – for instance, confuse the uncertainty in the estimate of the ensemble mean with the spread!

Particularly important simulations for model-data comparisons are the forced coupled-model runs for the 20th Century, and ‘AMIP’-style runs for the late 20th Century. ‘AMIP’ runs are atmospheric model runs that impose the observed sea surface temperature conditions instead of calculating them with an ocean model, optionally using other forcings as well and are particularly useful if it matters that you get the timing and amplitude of El NiƱo correct in a comparison. No more need the question be asked ‘what do the models say?’ – you can ask them directly.

The usefulness of any comparison is whether it really provides a constraint on the models and there are plenty of good examples of this. What is ideal are diagnostics that are robust in the models, not too affected by weather, and can be estimated in the real world e.g Ben Santer’s paper on tropospheric trends, the discussion we had on global dimming trends, and the AR4 report is full of more examples. What isn’t useful are short period and/or limited area diagnostics for which the ensemble spread is enormous.

CMIP3 2.0?

In such a large endeavor, it’s inevitable that not everything is done to everyone’s satisfaction and that in hindsight some opportunities were missed. The following items should therefore be read as suggestions for next time around, and not as criticisms of the organisation this time.

Initially the model output was only accessible to people who had registered and had a specific proposal to study the data. While this makes some sense in discouraging needless duplication of effort, it isn’t necessary and discourages the kind of casual browsing that is useful for getting a feel for the output or spotting something unexpected. However, the archive will soon be available with no restrictions and hopefully that setup can be maintained for other archives in future.

Another issue with access is the sheer amount amount of data and the relative slowness of downloading data over the internet. Here some lessons could be taken from more popular high-bandwidth applications. Reducing time-to-download for videos or music has relied on distributed access to the data. Applications like BitTorrent manage download speeds that are hugely faster than direct downloads because you end up getting data from dozens of locations at the same time, from people who’d downloaded the same thing as you. Therefore the more popular an item, the quicker it is to download. There is much that could be learned from this data model.

The other way to reduce download times is to make sure that you only download what is wanted. If you only want a time series of global mean temperatures, you shouldn’t need to download the two-dimensional field and create your own averages. Thus for many purposes, automatic global, zonal-mean or vertical averaging would have saved an enormous amount of time.

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