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about Gavin Schmidt

Gavin Schmidt is a climate modeler, working for NASA and with Columbia University.

Judy Curry’s attribution non-argument

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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

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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…

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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 overleaf.com 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?

Update: The crowd-sourced comment has appeared Köhler et al (2018).

References

  1. H. Harde, "Scrutinizing the carbon cycle and CO2 residence time in the atmosphere", Global and Planetary Change, vol. 152, pp. 19-26, 2017. http://dx.doi.org/10.1016/j.gloplacha.2017.02.009
  2. P. Köhler, J. Hauck, C. Völker, D.A. Wolf-Gladrow, M. Butzin, J.B. Halpern, K. Rice, and R.E. Zeebe, "Comment on “ Scrutinizing the carbon cycle and CO 2 residence time in the atmosphere ” by H. Harde", Global and Planetary Change, vol. 164, pp. 67-71, 2018. http://dx.doi.org/10.1016/j.gloplacha.2017.09.015

Someone C.A.R.E.S.

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Do we need a new venue for post-publication comments and replications?

Social media is full of commentary (of varying degrees of seriousness) on the supposed replication crisis in science. Whether this is really a crisis, or just what is to be expected at the cutting edge is unclear (and may well depend on the topic and field). But one thing that is clear from all the discussion is that it’s much too hard to publish replications, or even non-replications, in the literature. Often these efforts have to be part of a new paper that has to make its own independent claim to novelty before it can get in the door and that means that most attempted replications don’t get published at all.

This is however just a subset of the difficulty that exists in getting any kind of comment on published articles accepted. Having been involved in many attempts – in the original journal or as a new paper – some successful, many not, it has become obvious to me that the effort to do so is wholly disproportionate to the benefits for the authors, and is thus very effectively discouraged.

The overall mismatch between the large costs/minimal benefit for the commenters, compared to the real benefits for the field, suggests that something really needs to change.

I have thought for a long time that an independent journal venue for comments would be a good idea, but a tweet by Katharine Hayhoe last weekend made me realize that the replication issue might be well served by a similar approach. So, here’s a proposal for a new journal.

Commentary And Replication in Earth Science (C.A.R.E.S.)

[Read more…] about Someone C.A.R.E.S.

References

  1. G. Foster, J.D. Annan, G.A. Schmidt, and M.E. Mann, "Comment on “Heat capacity, time constant, and sensitivity of Earth's climate system” by S. E. Schwartz", Journal of Geophysical Research: Atmospheres, vol. 113, 2008. http://dx.doi.org/10.1029/2007JD009373
  2. G.A. Schmidt, "Spurious correlations between recent warming and indices of local economic activity", International Journal of Climatology, vol. 29, pp. 2041-2048, 2009. http://dx.doi.org/10.1002/joc.1831

Serving up a NOAA-thing burger

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I have mostly been sitting back and watching the John Bates story go through the predictable news-cycle of almost all supposed ‘scandalous’ science stories. The patterns are very familiar – an initial claim of imperfection spiced up with insinuations of misconduct, coordination with a breathless hyping of the initial claim with ridiculous supposed implications, some sensible responses refuting the initial specific claims and demolishing the wilder extrapolations. Unable to defend the nonsense clarifications are made that the initial claim wasn’t about misconduct but merely about ‘process’ (for who can argue against better processes?). Meanwhile the misconduct and data falsification claims escape into the wild, get more exaggerated and lose all connection to any actual substance. For sure, the technical rebuttals to the specific claims compete with balance of evidence arguments and a little bit of playful trolling for the attention of anyone who actually cares about the details. None of which, unfortunately, despite being far more accurate, have the narrative power of the original meme.

The next stages are easy to predict as well – the issues of ‘process’ will be lost in the noise, the fake overreaction will dominate the wider conversation and become an alternative fact to be regurgitated in twitter threads and blog comments for years, the originators of the issue may or may not walk back the many mis-statements they and others made but will lose credibility in any case, mainstream scientists will just see it as hyper-partisan noise and ignore it, no papers will be redacted, no science will change, and the actual point (one presumes) of the ‘process’ complaint (to encourage better archiving practices) gets set back because it’s associated with such obvious nonsense.

This has played out many, many times before: The Yamal story had a very similar dynamic, and before that the ‘1934‘ story, etc. etc.

Assuming for the sake of politeness that sound and fury signifying nothing is not the main goal for at least some participants, the question arises: since this is so predictable why do people still keep making the same mistakes?

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2016 Temperature Records

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To nobody’s surprise, all of the surface datasets showed 2016 to be the warmest year on record.



Barely more surprising is that all of the tropospheric satellite datasets and radiosonde data also have 2016 as the warmest year.



Coming as this does after the record warm 2015, and (slightly less definitively) record warm 2014, the three records in row might get you to sit up and pay attention.

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Tuning in to climate models

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There is an interesting news article ($) in Science this week by Paul Voosen on the increasing amount of transparency on climate model tuning. (Full disclosure, I spoke to him a couple of times for this article and I’m working on tuning description paper for the US climate modeling centers). The main points of the article are worth highlighting here, even if a few of the characterizations are slightly off.

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The Snyder Sensitivity Situation

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Nature published a great new reconstruction of global temperatures over the past 2 million years today. Snyder (2016) uses 61 temperature reconstructions from 59 globally diverse sediment cores and a correlation structure from model simulations of the last glacial maximum to estimate (with uncertainties) the history of global temperature back through the last few dozen ice ages cycles. There are multiple real things to discuss about this – the methodology, the relatively small number of cores being used (compared to what could have been analyzed), the age modeling etc. – and many interesting applications – constraints on polar amplification, the mid-Pleistocene transition, the duration and nature of previous interglacials – but unfortunately, the bulk of the attention will be paid to a specific (erroneous) claim about Earth System Sensitivity (ESS) that made it into the abstract and was the lead conclusion in the press release.

The paper claims that ESS is ~9ºC and that this implies that the long term committed warming from today’s CO2 levels is a further 3-7ºC. This is simply wrong.

[Read more…] about The Snyder Sensitivity Situation

References

  1. C.W. Snyder, "Evolution of global temperature over the past two million years", Nature, vol. 538, pp. 226-228, 2016. http://dx.doi.org/10.1038/nature19798

Why correlations of CO2 and Temperature over ice age cycles don’t define climate sensitivity

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We’ve all seen how well temperature proxies and CO2 concentrations are correlated in the Antarctic ice cores – this has been known since the early 1990’s and has featured in many high-profile discussions of climate change.



EPICA Dome C ice core greenhouse gas and isotope records.

The temperature proxies are water isotope ratios that can be used to estimate Antarctic temperatures and, via a scaling, the global values. The CO2 and CH4 concentration changes can be converted to radiative forcing in W/m2 based on standard formulas. These two timeseries can be correlated and the regression (in ºC/(W/m2)) has the units of climate sensitivity – but what does it represent?

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