Making Things Up?

[John Reece]

From Climate Etc.

The 50-50 argument
Posted on August 24, 2014 | 360 Comments

by Judith Curry

Pick one:

[a) Warming since 1950 is predominantly (more than 50%) caused by humans.

b) Warming since 1950 is predominantly caused by natural processes.

....

[Sparko]

c] There is no global warming.

[John Reece]

c] There is no global warming.

[Jedidiah]

Global warming is real and caused by cigarette smoking.

[John Reece]

Global warming is real and caused by cigarette smoking.

Another .

[John Reece]

Conclusion of the long OP link:

Attribution

I am arguing that climate models are not fit for the purpose of detection and attribution of climate change on decadal to multidecadal timescales. Figure 10.1 speaks for itself in this regard (see figure 11.25 for a zoom in on the recent hiatus). By ‘fit for purpose’, I am prepared to settle for getting an answer that falls in the right tercile.

The main relevant deficiencies of climate models are:

• climate sensitivity that appears to be too high, probably associated with problems in the fast thermodynamic feedbacks (water vapor, lapse rate, clouds)

• failure to simulate the correct network of multidecadal oscillations and their correct phasing

• substantial uncertainties in aerosol indirect effects

• unknown and uncertain solar indirect effects

So, how to sort this out and do a more realistic job of detecting climate change and and attributing it to natural variability versus anthropogenic forcing? Observationally based methods and simple models have been underutilized in this regard. Of great importance is to consider uncertainties in external forcing in context of attribution uncertainties.

The logic of reasoning about climate uncertainty, is not at all straightforward, as discussed in my paper Reasoning about climate uncertainty.

So, am I ‘making things up’? Seems to me that I am applying straightforward logic. Which IMO has been disturbingly absent in attribution arguments, that use climate models that aren’t fit for purpose, use circular reasoning in detection, fail to assess the impact of forcing uncertainties on the attribution, and are heavily spiced by expert judgment and subjective downweighting.