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Data rescue projects

Filed under: — gavin @ 17 August 2017

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.



Weather diary from the the Observatoire de Paris, written by Giovanni Cassini on 18th January 1789.

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).

Observations, Reanalyses and the Elusive Absolute Global Mean Temperature

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.

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Joy plots for climate change

Filed under: — gavin @ 22 July 2017

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:

So “joy plots” it is.

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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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References

  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. http://dx.doi.org/10.1126/sciadv.1602821

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).