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Extraordinary spring temperatures in Central Asia coincide with droughts and extreme floods in vast regions of Australia, and are greater than the size of Texas and fires in South Korea.
Researchers from the World Weather Attributes (WWA) group, which conducts rapid analysis of weather phenomena, said high temperatures in Central Asia are around 30°C and above 10c pre-industrial levels.
Climate change has made this “freakish” heatwave even more intense at around 4c, about three times more likely. They flagged these numbers as likely conservative estimates.
To map temperature data, rely on NASA’s Giovanni application.
The user-friendly interface makes it extremely easy to choose climate data for a particular area and period. There are a wide range of atmospheric (and other) variables with different temporal and spatial resolutions.
This may not absolutely provide all the metrics you need, but it’s a good one-stop shop for quick turnaround data graphics. Search results are visualized in the browser before you have the option to download the underlying data.
The resulting temperature anomaly data from the heat waves was drawn into QGIS, FT’s geospatial software, for styling. A diverging blue red color palette was used. This is a standard option for abnormal temperatures.
I’ve visualized the temperature anomaly data many times, so I wanted to see some additional data in a variety of ways.
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The UK has been in a very dry, warm, sunny spring so far, and since measurements began in 1910, the Met Office has recorded the sunnyest march in the UK (thirdest in the entire UK).
Although the weather is more than the climate graphics, this record has influenced the English sunshine chart.
Met Office publishes Sunshine data on its website and provides a regional breakdown. In addition to typical temperature parameters, this page includes sunshine, rainfall, rainy days and daily atmospheric frosts as monthly, seasonal, and annual series.
I downloaded the Sunshine data, reformatted it using programming language R, and then created a barcode plot for the monthly sunlight time. The month is the entire X-axis, with the monthly hours of sunlight along the Y-axis (inverse of mobile), and each tile represents the sunlight time of the month for the year. (NERD Note: I created this barcode plot using geom_tile from the ggplot2 library).
In this context, Ember Research Group has released monthly electricity data and interesting visuals that match it. The chart of electricity generation from UK solar power shows how sunny it was.
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