One of the most disruptive climate cycles is El Niño, which is a warm and rainy period that occurs roughly every four years. Intervals between two to seven years are common. El Niño is caused by changing ocean currents off the Pacific coast of central South America. Locally, it affects the fishing industry, agriculture, and interestingly the guano industry. The effects, however, are far-reaching, and can be observed around the globe. By looking at the interactive chart, you can pinpoint which years, and even which days were particularly warm near the coast of Peru, at the Chiclayo airport.
The oldest weather station in GHCN-daily, which is the data set used at Climate Binge, is the Brera Astronomical Observatory in Milan, in northern Italy. The data for the weather station begins in 1763, when the observatory was under construction. You can view the Milan data in the app here . The next oldest weather station is at the Clementinum in Prague. You can view the Prague data in the app here. Before Daniel Gabriel Fahrenheit invented the first mercury thermometer in 1714, weather data was imprecise. In order to get any data before that, we have to rely on things like tree ring analysis and Antarctic ice core analysis.
By looking at the chart, we can see that the winters of 1834/35 and 1835/36 were particularly long, meaning that the growing season of 1835 was short. These years are examples of the extreme cold weather brought on by the Little Ice Age, which lasted roughly between 1500 and 1900.
Have a look at the zipper line produced by leap days. The data from the Brera Astronomical Observatory is so old that you can see, in the zipper line, where leap days were skipped in 1800 and 1900.
Climate Binge also offers multi-dimensional interactive distribution plots. Move your mouse cursor over data points in the chart to find out more information about them such as the number of days a certain temperature was reached, and on what days. The following is a distribution plot showing temperature maximums from the Milan dataset.
I wouldn’t want to call this feature creep, because I have been wanting to enable data smoothing in the web app from the beginning. I was even using it in Excel, before I made it into a web app. Data smoothing is helpful because it removes noise, making the picture more clear, making it easier to see climate trends. I really like the effect that it has on contour maps, making them more continuous, so that you can see shapes in the picture. As with many other data visualization tools, noise reduction can be used improperly in order to mislead people. Don’t do it!
Here is an example using temperature maximums from the Guadeloupe islands in the Carribean. With a little bit of noise reduction applied, the shapes in the image are much more apparent. Head on over to the app to view the interactive version of this chart.
Gunnison, Colorado is a Rocky Mountain town that gets cold in the winter. Best of all, it has a weather station that has been operated since 1892, which is very old for a weather station, since most weather stations were not installed before 1950. The following chart shows temperature maximums for the entire history of the weather station. You can modify the parameters for the chart yourself in the web app.
The Gunnison weather station produces this next chart for temperature minimums. Using the crosshair tool in the interactive chart, the temperatures on specific days can be identified. It shows that it got unseasonably hot on June 12, 1987, when the temperature was 86°F, or 30°C. The year before that, it was 19.9°F, or -6.7°C, on the same day! Western Colorado is indeed a cold place to be in the winter. On February 14, 1954, the temperature dropped to -40.6 C. Incidentally, -40 is the temperature where the scales for Fahrenheit and Celcius cross. You can make your own chart in the web app, with the following link, for the Gunnison weather station.
There is also a chart for snow depth, which shows a vague 10-20 year cycle, and that it was especially snowy in 2007, when there was 97 inches of accumulated snow in January.
You might have noticed that the months labeled acrosss the bottom of the charts are out of order. How embarrassing! Usually that doesn’t happen, but the data is a little bit glitchy for Gunnison. It’s common for weather stations to have periods of missing data, but whoever looks after the weather station at Gunnison has trouble keeping track of calendar days. This is apparent because February 29 should have a missing record for each non-leap year, but instead the missing record is moved over a few days. There should be a clean, vertical dotted white line at February 29, but for the Gunnison chart, the line is not all there. I can just imagine some poor weather station keeper, isolated in his cabin, not knowing exactly what day it is, and writing down temperatures in his notebook. This has somehow messed up my chart output, so that the months are shown incorrectly.
In any case, I’m pretty happy with my web app. I especially like it that other people are able to use it to perform their own science and come to their own conclusions about climate trends. There’s a lot of distrust and skepticism towards the scientific community, which leads to a need for people to be able have a look at the data themselves without having to rely on what they read in the news. This website is the kind of DIY science tool that the world needs.
I was excited when I got my charting tool working, and I immediately started exploring the globe. Somehow I ended up in Guadeloupe, which is an archipelago of islands in the Carribean. One of the weather stations there was established in 1951 and continuously operated through at least 2000. It produces a chart that shows a rather obvious climate trend. Best of all, if you tick the “extrapolate data” button, the image looks a little bit like the moonlight reflecting on the sea. You can view and customize the chart in the web app.