Local Climate Sensitivities estimated from HadCRUT temperatures and CO2 concentrations using the ARX method. They are rounded to the nearest integer for display purposes. ( Figure 5 of my recent paper submitted to Proceedings of the Royal Society: A. )
Figure 5 is overwhelming evidence that CO2 does force temperature . No modeller has come up with anything close to Figure 5, because numerical models are just not good enough. They give a reasonable estimate of global climate sensitivity because they have been tweaked to do that.
I recently found that someone else has used statistical methods to map local climate sensitivity.
(Asinimov, O. A. (2001).”Predicting Patterns of Near-Surface Air Temperature Using Empirical Data”, Climatic Change 50: 297-315).
The is a striking resemblance to my Figure 5 above, particularly with regard to high values in Siberia and NW Canada.
Figure 3 shows that numerical model estimates of the long-term rate of removal of atmospheric carbon are hopelessly wrong (but politically correct). In fact, half of the CO2 in the atmosphere is removed every 43 years.
Download preprint: Reid2022
I originally submitted to PNAS who replied :
… This decision is necessarily subjective and does not reflect an evaluation of the technical quality of your work or of its appropriateness for a more specialized audience.
Thank you for submitting your work to PNAS; we wish you success in finding a more suitable venue for publication soon.
Proc. Roy Soc. A were not so kind. Evidently the paper is marred by its simplicity:
The author applied a statistical method to some climate time series and the conclusions are useful and consistent with what is already known. However, the statistical method is too simple and cannot provide relevant new insights into such a complex problem as climate change.
This encapsulates the anti-empirical head-set of the Climate Change industry: “I have made up my mind. Don’t confuse me with facts.” Ever heard of Occam’s Razor?
Although some conclusions can be drawn from the performed analysis, the causality of the conclusions is doubtful.
Easily fixed. The next iteration of the paper will include a section on Granger Causality which is well suited to the ARX methodology.
The paper raises an interesting question about the long term retention of CO2 in the atmosphere, in contrast to the result of an earlier publication. In future work I suggest that the author needs to establish more clearly why his result on the question of atmospheric retention of CO2 differs from that of Reference 11
Reference 11 is to Meier-Reimer and Hasselmann (1987) and we are talking about Figure 3 above. I did in fact establish why my result differs from theirs in the conclusion section, viz.:
A possible explanation is the following. The deep ocean is bounded by a turbulent mixed layer and by the highly turbulent Antarctic Circumpolar Current. It is therefore likely to be internally mixed by a Kolmogorov cascade of turbulent eddies, some with spatial scales as large as ocean basins and with time scales of, perhaps, decades. Turbulence is a stochastic phenomenon which is difficult to observe at large spatial and temporal scales and which, being stochastic, cannot be readily emulated by deterministic models such as OAGCMs. Eddy diffusion generated by turbulent mixing would greatly increase the capacity of the deep ocean to absorb carbon dioxide and so could account for the shorter half time of the observed impulse response of atmospheric CO2 concentration. Whatever the explanation, there is no evidence for the long half times and remnant component of atmospheric CO2 concentration in , presently assumed by most modellers.
I can only assume that the Editor had either not read that paragraph, or, having done so, had not understood it. Ideally the paper should have been reviewed by at least one person who could actually understand it.
It’s called “peer review”.