Climate science from climate scientists...
This month’s open thread. Topics this month? What should a conservative contrarian be writing op-eds about that avoids strawman arguments, and getting facts wrong? What do you really think about geoengineering? Tracking the imminent conclusion of the Nenana Ice Classic (background)?
Usual rules apply.
The Arctic is changing fast, and the Arctic Council recently commissioned the Arctic Monitoring and Assessment Programme (AMAP) to write two new reports on the state of the Arctic cryosphere (snow, water, and ice) and how the people and the ecosystems in the Arctic can live with these changes.
The two reports have now just been published and are called Snow Water Ice and Permafrost in the Arctic Update (SWIPA-update) and Adaptive Actions for a Changing Arctic (AACA).
[Read more…] about Snow Water Ice and Water and Adaptive Actions for a Changing Arctic
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.
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.
Guest commentary by Shaun Lovejoy (McGill University)
Below I summarize the key points of a new Climate Dynamics (CD) paper that I think opens up new perspectives on understanding and estimating the relevant uncertainties. The main message is that the primary sources of error and bias are not those that have been the subject of the most attention – they are not human in origin. The community seems to have done such a good job of handling the “heat island”, “cold park”, and diverse human induced glitches that in the end these make only a minor contribution to the final uncertainty. The reason of course, is the huge amount of averaging that is done to obtain global temperature estimates, this averaging essentially averages out most of the human induced noise.
Two tough sources of uncertainty remain: missing data and a poor definition of the space-time resolution; the latter leads to the key scale reduction factor. In spite of these large low frequency uncertainties, at centennial scales, they are still only about 13% of the IPCC estimated anthropogenic increase (with 90% certainty).
This paper is based on 6 monthly globally averaged temperature series over the common period 1880-2012 using data that were publically available in May 2015. These were NOAA NCEI, NASA GISTEMP, HadCRUT4, Cowtan and Way, Berkeley Earth and the 20th Century Reanalysis. In the first part on relative uncertainties, the series are systematically compared with each other over scales ranging from months to 133 years. In the second part on absolute uncertainties, a stochastic model is developed with two parts. The first simulates the true temperatures, the second treats the measurement errors that would arise from this series from three different sources of uncertainty: i) usual auto-regressive (AR)-type short range errors, ii) missing data, iii) the “scale reduction factor”.
The model parameters are fit by treating each of the six series as a stochastic realization of the stochastic measurement process. This yields an estimate of the uncertainty (spread) of the means of each series about the true temperature – an absolute uncertainty – not simply the spread of the series means about their common mean value (the relative uncertainty). This represents the absolute uncertainty of the series means about a (still unknown) absolute reference point (which is another problem for another post).
[Read more…] about What is the uncertainty in the Earth’s temperature rise?
Terms such as “gas skeptics” and “climate skeptics” aren’t really very descriptive, but they refer to sentiments that have something in common: unpredictable behaviour.
Gavin has already discussed John Christy’s misleading graph earlier in 2016, however, since the end of 2016, there has been a surge in interest in this graph in Norway amongst people who try to diminish the role of anthropogenic global warming.
I think this graph is warranted some extra comments in addition to Gavin’s points because it is flawed on more counts beyond those that he has already discussed. In fact, those using this graph to judge climate models reveal an elementary lack of understanding of climate data.
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 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.]
[Actually, it’s 30%.]
[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).