The AR4 attribution statement

Back in 2007, the IPCC AR4 SPM stated that:

“Most of the observed increase in global average temperatures since the mid-20th century is very likely due to the observed increase in anthropogenic greenhouse gas concentrations.”

This is a clear statement that I think is very well supported and correctly reflects the opinion of most climate scientists on the subject (and was re-affirmed in two recent papers (Jones and Stott, 2011;, Huber and Knutti, 2011)). It isn’t an isolated conclusion from a single study, but comes from an assessment of the changing patterns of surface and tropospheric warming, stratospheric cooling, ocean heat content changes, land-ocean contrasts, etc. that collectively demonstrate that there are detectable changes occurring which we can attempt to attribute to one or more physical causes.

Yet, in a paper just out in BAMS (Curry and Webster, 2011) this statement is apparently evidence that IPCC is unable to deal with uncertainty. Furthermore, Judith Curry has reiterated on her blog that the term ‘most’ is imprecise and undefined. For instance:

Apart from the undefined meaning of “most” in AR4 (which was subsequently clarified by the IPCC), the range 50.1-95% is rather imprecise in the context of attribution.

However, Curry’s argument is far from convincing, nor is it well formed (why is there a cap at 95%?). Nor was it convincing when I discussed the issue with her in the comments at Collide-a-Scape last year where she made similar points. Since the C&W paper basically repeats that argument (as has also been noticed by Gabi Hegerl et al who have a comment on the paper (Hegerl et al.)), it is perhaps worth addressing these specific issues again.

Let’s start with what the statement actually means. “Most” is an unambiguous adjective (meaning more than half), and ‘very likely’ in IPCC-speak means that the statement is being made with between 90 to 99% confidence (i.e. for every 10 such statements, the scientists expect 9 or more to pan out). Given that some people have found this confusing, it may help somewhat if the contents of the statement are visualised:

Figure 1: Two schematic distributions of possible ‘anthropogenic GHG contributions’ to the warming over the last 50 years. Note that in each case, despite a difference in the mean and variance, the probability of being below 50, is exactly 0.1 (i.e. a 10% likelihood).

The figure shows two Gaussian distributions, both of which have the probability of x being less than 50 at 0.1. i.e. P(x<50)=0.1. If either of them had been the distribution of the estimated increase in global temperatures due to anthropogenic greenhouse gas increases relative to the observed increase, the IPCC statement would have been almost exactly correct (i.e. if x=100*trend_caused_by_GHG/actual_trend). These distributions show a number of key issues that need to be appreciated. First, the actual increase of temperatures purely due to the rise in GHGs is not precisely known (and therefore there is a distribution of potential values). Note that we are presuming that there is a single ‘true’ answer, so the distribution is a measure of our ignorance, not a claim that the answer itself is a random variable.

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References

  1. G.S. Jones, and P.A. Stott, "Sensitivity of the attribution of near surface temperature warming to the choice of observational dataset", Geophysical Research Letters, vol. 38, pp. n/a-n/a, 2011. http://dx.doi.org/10.1029/2011GL049324
  2. M. Huber, and R. Knutti, "Anthropogenic and natural warming inferred from changes in Earth’s energy balance", Nature Geosci, vol. 5, pp. 31-36, 2011. http://dx.doi.org/10.1038/ngeo1327
  3. J.A. Curry, and P.J. Webster, "Climate Science and the Uncertainty Monster", Bulletin of the American Meteorological Society, vol. 92, pp. 1667-1682, 2011. http://dx.doi.org/10.1175/2011BAMS3139.1
  4. G. Hegerl, P. Stott, S. Solomon, and F. Zwiers, "Comment on “Climate Science and the Uncertainty Monster” J. A. Curry and P. J. Webster", Bulletin of the American Meteorological Society, vol. 92, pp. 1683-1685, 2011. http://dx.doi.org/10.1175/BAMS-D-11-00191.1