Archives par mot-clé : Anthropogenic influence

What Humans Contribute to Atmospheric CO2: Comparison of Carbon Cycle Models with Observations

by Herman Harde, April 3, 2019 in Earth Sciences


Abstract: The Intergovernmental Panel on Climate Change assumes that the inclining atmospheric CO2 concentration over

recent years was almost exclusively determined by anthropogenic emissions, and this increase is made responsible for the rising

temperature over the Industrial Era. Due to the far reaching consequences of this assertion, in this contribution we critically

scrutinize different carbon cycle models and compare them with observations. We further contrast them with an alternative

concept, which also includes temperature dependent natural emission and absorption with an uptake rate scaling proportional

with the CO2 concentration. We show that this approach is in agreement with all observations, and under this premise not really

human activities are responsible for the observed CO2 increase and the expected temperature rise in the atmosphere, but just

opposite the temperature itself dominantly controls the CO2 increase. Therefore, not CO2 but primarily native impacts are

responsible for any observed climate changes.

Keywords: Carbon Cycle, Atmospheric CO2 Concentration, CO2 Residence Time, Anthropogenic Emissions,

Fossil Fuel Combustion, Land Use Change, Climate Change

 

New Santer Study: 97% Consensus is now 99.99997%

by Dr. Roy Spencer, February 27, 2019 in GlobalWarming


A new paper in Nature Climate Change by Santer et al. (paywalled) claims that the 40 year record of global tropospheric temperatures agrees with climate model simulations of anthropogenic global warming so well that there is less than a 1 in 3.5 million chance (5 sigma, one-tailed test) that the agreement between models and satellites is just by chance.

And, yes, that applies to our (UAH) dataset as well.

While it’s nice that the authors commemorate 40 years of satellite temperature monitoring method (which John Christy and I originally developed), I’m dismayed that this published result could feed a new “one in a million” meme that rivals the “97% of scientists agree” meme, which has been a very successful talking point for politicians, journalists, and liberal arts majors.

John Christy and I examined the study to see just what was done. I will give you the bottom line first, in case you don’t have time to wade through the details:

The new Santer et al. study merely shows that the satellite data have indeed detected warming (not saying how much) that the models can currently only explain with increasing CO2 (since they cannot yet reproduce natural climate variability on multi-decadal time scales).

That’s all.

But we already knew that, didn’t we? So why publish a paper that goes to such great lengths to demonstrate it with an absurdly exaggerated statistic such as 1 in 3.5 million (which corresponds to 99.99997% confidence)? I’ll leave that as a rhetorical question for you to ponder.

Critique of the new Santer et al. (2019) paper

by Ross McKitrick, March1, 2019 in WUWT


Ben Santer et al. have a new paper out in Nature Climate Change arguing that with 40 years of satellite data available they can detect the anthropogenic influence in the mid-troposphere at a 5-sigma level of confidence. This, they point out, is the “gold standard” of proof in particle physics, even invoking for comparison the Higgs boson discovery in their Supplementary information.

Conclusion

The fact that in my example the t-statistic on anthro falls to a low level does not “prove” that anthropogenic forcing has no effect on tropospheric temperatures. It does show that in the framework of my model the effects are not statistically significant. If you think the model is correctly-specified and the data set is appropriate you will have reason to accept the result, at least provisionally. If you have reason to doubt the correctness of the specification then you are not obliged to accept the result.

This is the nature of evidence from statistical modeling: it is contingent on the specification and assumptions. In my view the second regression is a more valid specification than the first one, so faced with a choice between the two, the second set of results is more valid. But there may be other, more valid specifications that yield different results.

In the same way, since I have reason to doubt the validity of the Santer et al. model I don’t accept their conclusions. They haven’t shown what they say they showed. In particular they have not identified a unique anthropogenic fingerprint, or provided a credible control for natural variability over the sample period. Nor have they justified the use of Gaussian p-values. Their claim to have attained a “gold standard” of proof are unwarranted, in part because statistical modeling can never do that, and in part because of the specific problems in their model.