Global Warming as you’ve never seen it before
Working on the world’s weather observations means I spend a lot of time looking at maps. I like the equirectangular (plate carrée) projection (fills the screen nicely, latitude and longitude are all you need to know), but it does have a couple of diadvantages: Map geeks disdain it as both boring and badly distorted, and it’s hopeless for looking at the Arctic and Antarctic.
You can work around both of these problems by the technical trick of ‘rotating the pole’. There is no fundamental reason why a map has to have the North Pole at the top. If you rotate your globe so that some other point is at the top before performing the projection that turns it into a flat map; you can make a map that is still equirectangular, but looks very different, and has the Arctic (or location of your choice) in the middle. It’s no less distorted, but it is less boring, as the distortion has moved into different places.
HadCRUT is a global temperature monitoring dataset. We use it to keep track of global warming, amongst other purposes. It combines thermometer observations, from ships and land weather stations, to make estimates of temperature change month-by-month back to 1850. The sea-temperature observations we are rescuing in oldWeather will be used to improve HadCRUT.
HadCRUT is constructed on a regular grid on a conventional equirectangular map. Looking at it on a map with a rotated (and rotating) pole gives a fresh look at what we know about global temperature change (and a sharp reminder of the problems with map projections). I like this visualisation because not only does the changing observation coverage show the same sort of historical effects we’ve already seen in the pressure observations, but it illustrates what we know and what we don’t about past temperature: The growing global warming is unmistakable in the last few decades, in spite of the large regional variability and observational uncertainties, but smaller-scale changes, further back in time, can still have large uncertainty – new observations could make a big difference.