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The Soon fallacy

Filed under: — gavin @ 24 February 2015

As many will have read, there were a number of press reports (NYT, Guardian, InsideClimate) about the non-disclosure of Willie Soon’s corporate funding (from Southern Company (an energy utility), Koch Industries, etc.) when publishing results in journals that require such disclosures. There are certainly some interesting questions to be asked (by the OIG!) about adherence to the Smithsonian’s ethics policies, and the propriety of Smithsonian managers accepting soft money with non-disclosure clauses attached.

However, a valid question is whether the science that arose from these funds is any good? It’s certainly conceivable that Soon’s work was too radical for standard federal research programs and that these energy companies were really taking a chance on blue-sky high risk research that might have the potential to shake things up. In such a case, someone might be tempted to overlook the ethical lapses and conflicts of interest for the sake of scientific advancement (though far too many similar post-hoc justifications have been used to excuse horrific unethical practices for this to be remotely defendable).

Unfortunately, the evidence from the emails and the work itself completely undermines that argument because the work and the motivation behind it are based on a scientific fallacy.
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Absolute temperatures and relative anomalies

Filed under: — gavin @ 23 December 2014

Most of the images showing the transient changes in global mean temperatures (GMT) over the 20th Century and projections out to the 21st C, show temperature anomalies. An anomaly is the change in temperature relative to a baseline which usually the pre-industrial period, or a more recent climatology (1951-1980, or 1980-1999 etc.). With very few exceptions the changes are almost never shown in terms of absolute temperatures. So why is that?

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Climate response estimates from Lewis & Curry

Guest commentary from Richard Millar (U. Oxford)

The recent Lewis and Curry study of climate sensitivity estimated from the transient surface temperature record is being lauded as something of a game-changer – but how much of a game-changer is it really?

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References

  1. N. Lewis, and J.A. Curry, "The implications for climate sensitivity of AR5 forcing and heat uptake estimates", Clim Dyn, 2014. http://dx.doi.org/10.1007/s00382-014-2342-y

IPCC attribution statements redux: A response to Judith Curry

Filed under: — gavin @ 27 August 2014

I have written a number of times about the procedure used to attribute recent climate change (here in 2010, in 2012 (about the AR4 statement), and again in 2013 after AR5 was released). For people who want a summary of what the attribution problem is, how we think about the human contributions and why the IPCC reaches the conclusions it does, read those posts instead of this one.

The bottom line is that multiple studies indicate with very strong confidence that human activity is the dominant component in the warming of the last 50 to 60 years, and that our best estimates are that pretty much all of the rise is anthropogenic.



The probability density function for the fraction of warming attributable to human activity (derived from Fig. 10.5 in IPCC AR5). The bulk of the probability is far to the right of the “50%” line, and the peak is around 110%.

If you are still here, I should be clear that this post is focused on a specific claim Judith Curry has recently blogged about supporting a “50-50″ attribution (i.e. that trends since the middle of the 20th Century are 50% human-caused, and 50% natural, a position that would center her pdf at 0.5 in the figure above). She also commented about her puzzlement about why other scientists don’t agree with her. Reading over her arguments in detail, I find very little to recommend them, and perhaps the reasoning for this will be interesting for readers. So, here follows a line-by-line commentary on her recent post. Please excuse the length.
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Labels for climate data

Filed under: — rasmus @ 24 April 2014

metadata-fig “These results are quite strange”, my colleague told me. He analysed some of the recent climate model results from an experiment known by the cryptic name ‘CMIP5‘. It turned out that the results were ok, but we had made an error when reading and processing the model output. The particular climate model that initially gave the strange results had used a different calendar set-up to the previous models we had examined.

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