Archives par mot-clé : Anthropogenic influence

How much human-caused global warming should we expect?

by Andy May, March 21, 2020 in WUWT


C3S20 asks, how much human-caused warming will occur if we do nothing, that is, continue “business-as-usual?” It’s unfortunate, but the IPCC, for all their work, do not adequately answer that question, their projections are all based on abstract “scenarios.” C3S20 break this overall question into five parts:

  1. What would greenhouse gas emissions (GHG) be, if we did nothing and continued normally?
  2. For each GHG, how do emissions relate to the change in atmospheric concentration?
  3. What would the global temperature be if GHG concentrations were at “preindustrial” levels?
  4. How sensitive are global temperatures to GHG concentrations?
  5. How much warming should we expect if we do nothing?

C3S20 tell us the Paris Agreement conclusion that we need to limit global warming to 2°C above preindustrial levels suffers from several unknowns.

  1. The preindustrial period is not formally defined. The preindustrial temperature and greenhouse gas level are not specified. In fact, several time periods, temperatures and GHG levels are used as “preindustrial” in the latest IPCC AR5 report.

  2. The assumptions that warming is bad and increasing levels of CO2 are bad, are not supported with any data. Numerous studies have concluded that some warming is good for humankind and additional CO2 is good for plants.

  3. The penultimate draft of AR5 identified the period 1850 to 1900 as the preindustrial baseline for CO2 and temperature. This was the end of the Little Ice Age, the coldest periodin the last several thousand years. Why use that period as a baseline (Luning and Vahrenholt 2017)? This is not explained, and the final draft of the report removed the reference to the 1850 to 1900 baseline.

  4. If the UNFCC and the Paris Agreement assume “climate change” and “Human-caused Climate Change” are synonymous, how do they explain that climate has change much quicker and much more dramatically many times in the past 13,000 years before human civilization began and well before industrialization?

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.