Shifting Distribution of Northern Hemisphere Summer Temperature Anomalies, 1951-2011

This visualization shows how the distribution of land temperature anomalies has varied over time. As the visualization plays, we see the distribution shifting to the right as the Earth warms. The peak goes down as the distribution broadens, indicating that temperature variations are becoming more extreme. The broadening of this distribution is due to differential regional warming rather than increased temperature variability at any given location. We also see the distribution oscillate back and forth. This is the imprint of the El Niño-Southern Oscillation (ENSO). The planet is warmer in the El Niño phase, and cooler in the La Niña phase. These distributions are calculated from the Goddard Institute of Space Studies GISTEMP surface temperature analysis. Distributions are determined for each year using a kernal density esitmator, and we morph between those distributions in the animation.NASA’s full surface temperature data set – and the complete methodology used to make the temperature calculation – are available at: https://data.giss.nasa.gov/gistemp. A python based Jupyter Notebook which access the data and creates these visualizations is available. Click here to download.GISS is a NASA laboratory managed by the Earth Sciences Division of the agency’s Goddard Space Flight Center in Greenbelt, Maryland. The laboratory is affiliated with Columbia University’s Earth Institute and School of Engineering and Applied Science in New York.Descriptive Text for the VisualizationThe visualizations starts with a graph showing the global distribution of temperature anomalies. At the top of the graph is a title that reads ‘Land Temperature Anomaly Distribution’ and at the bottom is the x-axis label that reads ‘Temperature Anomaly in Celsius’. The distribution is bell shaped, with a peak just slightly left of the center zero-degree mark, rising nearly to the top of the graph. The width of the bell-shaped distribution is approximately one degree. The area under the curve is colored with the area to the left of the center zero line progressively darker and darker shades of blue and the area to the right of center progressively darker and darker shades of red representing warmer temperatures. In the upper right a label indicates showing the year, that label starts at 1962. As the visualization plays the year runs from 1962 to 2022, and the distribution wobbles back and forth and bobs up and down. The motion has led to this visualization being nicknamed ‘the climate jellyfish’. In addition to the back and forth and up and down motion, as the visualization progresses the visualization progressively shifts to the right and broadens. Every 20 years a grey trace of the distribution is left behind, illustrating the change over time. Once the animation reaches 2022 the peak is roughly at 1.2 degree Celsius, and the distribution has broadened so that it is now almos twice as wide as it was in 1962 and roughly two-thirds as high. The change in the distribution of land temperature anomalies over the years 1962 to 2022.

This visualization shows how the distribution of land temperature anomalies has varied over time. As the planet has warmed, we see the peak of the distribution shifting to the right. The distribution of temperatures broadens as well. This broadening is most likely due to differential regional warming rather than increased temperature variability at any given location.These distributions are calculated from the Goddard Institute of Space Studies GISTEMP surface temperature analysis. Distributions are determined for each year using a kernal density esitmator, and we morph between those distributions in the animation.NASA’s full surface temperature data set – and the complete methodology used to make the temperature calculation – are available at: https://data.giss.nasa.gov/gistempGISS is a NASA laboratory managed by the Earth Sciences Division of the agency’s Goddard Space Flight Center in Greenbelt, Maryland. The laboratory is affiliated with Columbia University’s Earth Institute and School of Engineering and Applied Science in New York.The python based Jupyter Notebook used to create these visualizations is available. Click here to download. The change in the distribution of land temperature anomalies over the years 1951 to 2020 A ridgeline plot showing how the land temperature anomaly distribution has changed over seven decades