What we do not know in terms of adaptation

A recent paper by Oreskes et al. in the journal Philosophy of Science asserts that “there is a gap between the scale on which models produce consistent information and the scale on which humans act”. While the large scales, such as the global mean, provide the best indicators of the state of earth’s climate, it is on the local scales we feel a climate change, such as floods and extreme weather events. Extreme rainfall is usually local. So how is it possible then, as two new papers in Nature by Min et al. and Pall et al. (discussed here) have done, to attribute extreme precipitation and extreme UK floods to climate change?

First of all, Oreskes et al. emphasize that the reality of mean global warming is essentially undisputed, but that the future impacts on the scale for which humans would have to prepare are still the subject of considerable research, inquiry, and debate. Moreover, they argue that climate models do not give us the information we would need to accurately estimate the costs of adaptation and effectively prepare for the consequences of climate change – successful adaptation to future climate changes depends on whether the models produce realistic projections for regional and local scales.

We have already discussed why climate models are not well suited for providing detailed information about local climate on RC (here and here). It is important to keep in mind that models are only approximate representation of the real world, and that they are only meant to capture the essence of our climate – i.e. the larger picture. There will always be a limit to the degree of detail for which the models fail to produce reliable and useful information, and the interesting question is where this limit is. It’s a question of limitation rather than flaw.

There is a difference between the spatial scales associated with a local point measurement and statistics based on many local values. When looking at the statistics for a large region, one could argue that these studies do not rely on local scales. In fact, Min et al. used leading empirical orthogonal functions (EOFs; a type of principal component analysis) in their attribution analysis for extreme precipitation, implying large spatial scales. Hence, the points raised by Oreskes et al. may perhaps not be directly applicable to the attribution study done by Min et al.

Pall et al., however, involved statistical downscaling to bridge the scaling gap between model and real world. Oreskes et al. paper argues that even with downscaling, our information about local scales is incomplete. Hence, the points raised by Oreskes et al. may be more relevant for the study of Pall et al. – and indeed for several of my own papers (e.g. local temperature scenarios available for viewing in GoogleEarth described in a forthcoming publication).

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