Climate science from climate scientists...
This is a thread to discuss the surface temperature records that were all released yesterday (Jan 18). There is far too much data-vizualization on this to link to, but feel free to do so in the comments. Bottom line? It’s still getting warmer.
[Update: the page of model/observational data comparisons has now been updated too.]
If you think you know why NASA did not report the discovery of the Antarctic polar ozone hole in 1984 before the publication of Farman et al in May 1985, you might well be wrong.
One of the most fun things in research is what happens when you try and find a reference to a commonly-known fact and slowly discover that your “fact” is not actually that factual, and that the real story is more interesting than you imagined…
[Read more…] about What did NASA know? and when did they know it?
Some concerns about continued monitoring of sea ice by remote sensing were raised this week in Nature News an article in the (UK) Observer: Donald Trump accused of obstructing satellite research into climate change. The last headline is not really correct, but the underlying issues are real.
Back in February we highlighted an obviously wrong paper by Harde which purported to scrutinize the carbon cycle. Well, thanks to a crowd sourced effort which we helped instigate, a comprehensive scrutiny of those claims has just been published. Lead by Peter Köhler, this included scientists from multiple disciplines working together to clearly report on the mistaken assumptions in the Harde paper.
The comment is excellent, and so should be well regarded, but the fact that it is a comment means that the effort will likely be sorely underappreciated. Part of problem is the long time for the process (almost 8 months) which means that the nonsense is mostly forgotten about by the time the comments are published. We’ve discussed trying to speed up and improve the process by having a specialized journal for comments and replications but really the problem here is the low quality of peer review and editorial supervision that allows these pre-rebunked papers to appear in the first place.
GPC is not the only (nor the worst) culprit for this kind of nonsense – indeed we just noticed a bunch of astrology papers in the International Journal of Heat and Technology (by Nicola Scatetta [natch]). It does seem to demonstrate that truly you can indeed publish anything somewhere.
It’s often been said that while we can only gather new data about the planet at the rate of one year per year, rescuing old data can add far more data more quickly. Data rescue is however extremely labor intensive. Nonetheless there are multiple data rescue projects and citizen science efforts ongoing, some of which we have highlighted here before. For those looking for an intro into the subject, this 2014 article is an great introduction.
I was asked this week whether there was a list of these projects, and with a bit of help from Twitter, we came up with the following:
(If you know of any more, please add them in the comments, and I’ll try and keep this list up to date).
One of the most common questions that arises from analyses of the global surface temperature data sets is why they are almost always plotted as anomalies and not as absolute temperatures.
There are two very basic answers: First, looking at changes in data gets rid of biases at individual stations that don’t change in time (such as station location), and second, for surface temperatures at least, the correlation scale for anomalies is much larger (100’s km) than for absolute temperatures. The combination of these factors means it’s much easier to interpolate anomalies and estimate the global mean, than it would be if you were averaging absolute temperatures. This was explained many years ago (and again here).
Of course, the absolute temperature does matter in many situations (the freezing point of ice, emitted radiation, convection, health and ecosystem impacts, etc.) and so it’s worth calculating as well – even at the global scale. However, and this is important, because of the biases and the difficulty in interpolating, the estimates of the global mean absolute temperature are not as accurate as the year to year changes.
This means we need to very careful in combining these two analyses – and unfortunately, historically, we haven’t been and that is a continuing problem.
[Read more…] about Observations, Reanalyses and the Elusive Absolute Global Mean Temperature
This is joy as in ‘Joy Division’, not as in actual fun.
Many of you will be familiar with the iconic cover of Joy Division’s Unknown Pleasures album, but maybe fewer will know that it’s a plot of signals from a pulsar (check out this Scientific American article on the history). The length of the line is matched to the frequency of the pulsing so that successive pulses are plotted almost on top of each other. For many years this kind of plot did not have a well-known designation until, in fact, April this year:
I hereby propose that we call these "joy plots" #rstats https://t.co/uuLGpQLAwY
— Jenny Bryan (@JennyBryan) April 25, 2017
So “joy plots” it is.
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.
[Read more…] about Climate Sensitivity Estimates and Corrections
) 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).
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.