Excess Wind At ERL

  • Date: 17/05/18
  • Andrew Montford, GWPF

A number of commenters on social media are posting this morning about a new paper that claims that there is the potential for 10% more onshore wind energy to be produced in the UK in a world that is 1.5 degrees warmer.

The paper, by Hosking et al., is published in Environmental Research Letters, a journal that has a reputation for emphasising the “environmental” over the “research” and it certainly doesn’t disappoint on this occasion.

The authors note that CMIP5 climate models are “not wholly suited to the task of assessing regional impacts with a 1.5 °C warming scenario”. They might have mentioned that the same caveat applies to all such models and to all climate change scenarios: no climate simulation has proven capable of telling us anything about regional climate change. Models are “tuned” (for which read “fudged”) to get some global average – typically temperature – correct and the local “difficulties” are left to fend for themselves. As an example, here is what the people behind one of the models used by Hosking et al. had to say about their own product:

[ECHAM6 has] poor representation of low‐level clouds, systematic shifts in major precipitation features, biases in the partitioning of precipitation between land and sea (particularly in the tropics), and midlatitude jets that appear to be insufficiently poleward.

If the midlatitude jets are awry then it’s perhaps unsurprising that this model, along with all the others, gets average wind speeds wrong. Fortunately, some of the modelling groups have “bias corrected” their outputs. In other words, they have tweaked the numbers through some whizz-bang statistical procedure to make them look a bit more like reality:

In this study we only use those models where daily mean 10 m wind speed has been locally bias-corrected by using the ‘Inter-Sectoral Impact Model Intercomparison Project’ ISIMIP2b calibration methodology (Lange 2016).

So the recipe is as follows:

  1. Build model
  2. Fudge to make global average on interest look right
  3. Fudge to make local measure of interest look right
  4. Make inferences about future.
  5. Move swiftly onwards and hope nobody checks your work.
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