What is the Yearly Temperature Anomaly Distribution?

This is the geographical breakdown of regional temperature anomalies around the Earth per year. Each temperature anomaly is the difference between the average yearly surface temperature and its pre-industrial baseline for that area. The pre-industrial baseline is calculated as the average temperature from 1850 to 1900.

The regional temperature anomalies on a yearly average are a result of many underlying factors:

• Global warming: This is due to the reduction in the Earth’s outgoing energy because of the increase in crucial greenhouse gas concentrations, like carbon dioxide (CO₂), methane (CH₄), nitrous oxide (N₂O) and others.
• Climate and weather patterns: These include El Niño, La Niña, currents, jet streams and more.
• Direct solar reflection: For example, aerosols and sulfur dioxide from volcanic eruptions and pollution cool the planet by directly reflecting sunlight back into space. Similar or opposite effects can come from Earth surface changes and other phenomena.
• Feedback loops: For instance, global warming can melt the reflective ice cover, creating an energy-absorbing ocean that reduces direct solar reflection. Many more feedback loops exist and they can be very complex.

The Yearly Temperature Anomaly Distribution is important because not all areas of the Earth are warming equally, and not all areas respond the same to the warming. For example, the polar regions are warming faster than the global average, which accelerates the ice melt. Understanding regional temperature increases will be one of many crucial factors for humanity’s ability to deal with regional and global climate change disasters.

Units and measures

Degree Celsius (°C) or degree Fahrenheit (°F) per year per area. Each dot represents an area of approximately 180 km by 180 km, which is around 32,000 km².

Insights from this chart

Poles

The poles are warming faster due to polar amplification. In 2012, the warming at the North Pole was extreme. In almost all years since 2000, the effect has been clearly visible.

Northern hemisphere

The northern hemisphere warms faster than the southern hemisphere. Different types of land cover, like ocean, land and ice, cause differences in regional warming. For example, the southern hemisphere is dominated by oceans and is warming more slowly than the northern hemisphere, which has a lot of land mass.

Greenhouse gases and global warming

Greenhouse gases cause global warming, but regional emissions do not specifically warm those regions because greenhouse gases spread quickly around the world.

Warm years

In 2023 you can see some of the highest anomalies so far. It was the warmest year since the pre-industrial era and had the highest global and regional anomalies. It is full of orange, red and violet areas, representing anomalies up to 8 °C (14.4 °F). Such high levels can only be observed in the last few decades.

Cold years

The year 1904 is an example of a cold year, where the majority of the globe is colored blue.

In 1964 you can explore a rather cool year, which occurred during a period of global dimming.

About the data

The data are an average of four sources that are used by IPCC and IGCC for temperature anomalies: HadCRUT5, NOAAGlobalTemp, Berkeley Earth and Kadow et al.. Each of these sources have gridded datasets. The underlying data is gathered using a huge number of weather stations on land, ships, buoys and more.

Two of the sources, NOAAGlobalTemp and Kadow et al., provide a complete coverage of data across the whole globe since 1850. HadCRUT5 and Berkeley Earth have many blank values for remote areas before 1950. The data that we show is the average of the available data sources. Since 1950 the scientific and technological methods of observing our planet have significantly improved and all sources have high coverage and confidence.

The value for the current year is actually the average for the last 12 months, for example in March we include values since the previous April. This approach allows us to include the latest data for a full year and avoids showing possibly misleading values when using shorter time periods.

Data sources

HadCRUT5 Met Office Hadley Centre
Credits: Morice, C.P., J.J. Kennedy, N.A. Rayner, J.P. Winn, E. Hogan, R.E. Killick, R.J.H. Dunn, T.J. Osborn, P.D. Jones and I.R. Simpson (in press) An updated assessment of near-surface temperature change from 1850: the HadCRUT5 dataset. Journal of Geophysical Research (Atmospheres) doi:10.1029/2019JD032361Update cycle: monthlyDelay: Less than 1 month

NOAA Global Temp. v5.1 NOAA's National Centers for Environmental Information
Credits: R. S. Vose, B. Huang, X. Yin, D. Arndt, D. R. Easterling, J. H. Lawrimore, M. J. Menne, A. Sanchez-Lugo, and H. M. Zhang (2022): NOAA Global Surface Temperature Dataset (NOAAGlobalTemp), Version 5.1 Gridded. NOAA National Centers for Environmental Information. doi.org/10.25921/2tj4-0e21.Update cycle: monthlyDelay: Less than 1 month

Temperature Data Berkeley Earth
Credits: Rohde, R. A. and Hausfather, Z.: The Berkeley Earth Land/Ocean Temperature Record, Earth Syst. Sci. Data, 12, 3469–3479, https://doi.org/10.5194/essd-12-3469-2020, 2020.Update cycle: monthlyDelay: ~ 1 to 4 month

Kadow et al. Kadow et al.
Credits: Kadow, C., Hall, D.M. & Ulbrich, U. Artificial intelligence reconstructs missing climate information. Nat. Geosci. 13, 408–413 (2020). https://doi.org/10.1038/s41561-020-0582-5