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Climate Sensitivity Estimates and Corrections

You need to be careful in inferring climate sensitivity from observations.

Two climate sensitivity stories this week – both related to how careful you need to be before you can infer constraints from observational data. (You can brush up on the background and definitions here). Both cases – a “Brief Comment Arising” in Nature (that I led) and a new paper from Proistosescu and Huybers (2017) – examine basic assumptions underlying previously published estimates of climate sensitivity and find them wanting.

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  1. C. Proistosescu, and P.J. Huybers, "Slow climate mode reconciles historical and model-based estimates of climate sensitivity", Science Advances, vol. 3, pp. e1602821, 2017.

Nenana Ice Classic 2017

Filed under: — gavin @ 2 May 2017 - (Español)

As I’ve done for a few years, here is the updated graph for the Nenana Ice Classic competition, which tracks the break up of ice on the Tanana River near Nenana in Alaska. It is now a 101-year time series tracking the winter/spring conditions in that part of Alaska, and shows clearly the long term trend towards earlier break up, and overall warming.

2017 was almost exactly on trend – roughly one week earlier than the average break up date a century ago. There was a short NPR piece on the significance again this week, but most of the commentary from last year and earlier is of course still valid.

My shadow bet on whether any climate contrarian site will mention this dataset remains in play (none have since 2013 which was an record late year).

Judy Curry’s attribution non-argument

Filed under: — gavin @ 18 April 2017

Following on from the ‘interesting’ House Science Committee hearing two weeks ago, there was an excellent rebuttal curated by ClimateFeedback of the unsupported and often-times misleading claims from the majority witnesses. In response, Judy Curry has (yet again) declared herself unconvinced by the evidence for a dominant role for human forcing of recent climate changes. And as before she fails to give any quantitative argument to support her contention that human drivers are not the dominant cause of recent trends.

Her reasoning consists of a small number of plausible sounding, but ultimately unconvincing issues that are nonetheless worth diving into. She summarizes her claims in the following comment:

… They use models that are tuned to the period of interest, which should disqualify them from be used in attribution study for the same period (circular reasoning, and all that). The attribution studies fail to account for the large multi-decadal (and longer) oscillations in the ocean, which have been estimated to account for 20% to 40% to 50% to 100% of the recent warming. The models fail to account for solar indirect effects that have been hypothesized to be important. And finally, the CMIP5 climate models used values of aerosol forcing that are now thought to be far too large.

These claims are either wrong or simply don’t have the implications she claims. Let’s go through them one more time.

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Model projections and observations comparison page

Filed under: — gavin @ 11 April 2017

We should have done this ages ago, but better late than never!

We have set up a permanent page to host all of the model projection-observation comparisons that we have monitored over the years. This includes comparisons to early predictions for global mean surface temperature from the 1980’s as well as more complete projections from the CMIP3 and CMIP5. The aim is to maintain this annually, or more often if new datasets or versions become relevant.

We are also happy to get advice on stylistic choices or variations that might make the graphs easier to comprehend or be more accurate – feel free to suggest them in the comments below (since the page itself will be updated over time, it doesn’t have comments associated with it).

If there are additional comparisons you are aware of that you think would be useful to include, please point to the model and observational data set(s) and we’ll try and include that too. We should have the Arctic sea ice trends up shortly for instance.

Something Harde to believe…

Filed under: — gavin @ 25 February 2017

A commenter brings news of an obviously wrong paper that has just appeared in Global and Planetary Change. The paper purports to be a radical revision of our understanding of the carbon cycle by Hermann Harde. The key conclusions are (and reality in green):

  • The average residence time of CO2 in the atmosphere is found to be 4 years.

    [The residence time for an individual molecule is not the same as the perturbation response time of the carbon cycle which has timescales of decades to thousands of years.]

  • The anthropogenic fraction of CO2 in the atmosphere is only 4.3%.

    [Actually, it’s 30%.]

  • Human emissions only contribute 15% to the CO2 increase over the Industrial Era.

    [It’s all of it.]

Since these points contradict multiple independent sources of evidence, I can, without hesitation, predict that there are fundament flaws in this paper that will raise serious questions about the quality of the peer-review that this paper went through. Oddly, this paper is labeled as an “Invited Research Article” and so maybe some questions might be asked of the editor responsible too.

Notwithstanding our last post on the difficulty in getting comments published, this paper is crying out for one.

But this kind of thing has been done before, does not require any great sophistication or computer modeling to rebut, and has come up so many times before (Salby (also here), Beck, Segalstad, Jaworowski etc.), that perhaps a crowd-sourced rebuttal would be useful.

So, we’ll set up an page for this (a site for collaborative LaTeX projects), and anyone who wants to contribute should put the gist of their point in the comments and we’ll send the link so you can add it to the draft. Maybe the citizen scientists among you can pull together a rebuttal faster than the professionals?


  1. H. Harde, "Scrutinizing the carbon cycle and CO 2 residence time in the atmosphere", Global and Planetary Change, vol. 152, pp. 19-26, 2017.