Itâ€™s incredible that a global warming theory could agree with both the IPCC (discernable anthropogenic influence) and the sceptics (low long term risk from emissions) but there you are. The analysis of Greenstock suggests it is not the amount of greenhouse gasses, particularly CO2, in the atmosphere that contributes to global warming, but the change in the amount. That is, when the rate of CO2 produced is increasing — as it was last century — this increases the global temperature. Conversely, if the rate of increase is constant so is temperature.
The graph above illustrates the global temperature (CRU) and the rate of increase of CO2 at Mauna Loa (dCO2). Greenstockâ€™s theory provides an explanation for the moderation of temperature increases in the last decade. Anthropogenic effects are predicted to decline in coming decades, if the rate of increase in CO2 remains linear, or slows. As cohenite suggested, the theory suggests no problems with longer term delayed effects, apart from those involved in the processes that compensate for the level CO2.
The most obvious physical explanation is a process that adjusts to an impulse of GHGs into the atmosphere, restoring the radiation balance as it was before the impulse. In this case, there is an obvious relationship to Miskolcziâ€™s theory of constant greenhouse effect, where levels of water vapour in the upper atmosphere adjust to maintain a constant greenhouse effect. Various negative feedback mechanisms involving clouds could also be implicated.
Obviously few people expect one unpublished paper to turn decades of thinking on its head. But if Greenstock is right, it means climate science has screwed up, big time. There are very many next steps, the first of which would be for Greenstock to get the manuscript published. Improved statistical tests of integration order, evaluation of other data, such as satellite data and global climate simulations, and integration of these results into other theories proposing a homeostatic climatic system are warranted. Cointegration is a generalization of correlation, only not so prone to spurious regression. It should have great applicability of climate studies.