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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).

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.