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Monthly temperature anomalies

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About this data

Monthly temperature anomalies
The deviation of a specific month's average surface temperature from the 1991-2020 mean, in degrees Celsius.
Source
Copernicus Climate Change Service (2019) – with major processing by Our World in Data
Last updated
December 20, 2023
Unit
°C

Related research and writing

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How much have temperatures risen in countries across the world?

Veronika Samborska

Sources and processing

This data is based on the following sources

ERA5 is the latest climate reanalysis produced by ECMWF, providing hourly data on many atmospheric, land-surface and sea-state parameters together with estimates of uncertainty.

ERA5 data are available in the Climate Data Store on regular latitude-longitude grids at 0.25° x 0.25° resolution, with atmospheric parameters on 37 pressure levels.

ERA5 is available from 1940 and continues to be extended forward in time, with daily updates being made available 5 days behind real time

Initial release data, i.e., data no more than three months behind real time, are called ERA5T.

Retrieved on
June 7, 2024
Retrieved from
https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels-monthly-means?tab=overview
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2023): ERA5 monthly averaged data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.f17050d7 (Accessed on 07-June-2024)

How we process data at Our World in Data

All data and visualizations on Our World in Data rely on data sourced from one or several original data providers. Preparing this original data involves several processing steps. Depending on the data, this can include standardizing country names and world region definitions, converting units, calculating derived indicators such as per capita measures, as well as adding or adapting metadata such as the name or the description given to an indicator.

At the link below you can find a detailed description of the structure of our data pipeline, including links to all the code used to prepare data across Our World in Data.

Read about our data pipeline
Notes on our processing step for this indicator

  • Temperature measured in kelvin was converted to degrees Celsius (°C) by subtracting 273.15.

  • Initially, the temperature dataset is provided with specific coordinates in terms of longitude and latitude. To tailor this data to each country, we utilize geographical boundaries as defined by the World Bank. The method involves trimming the global temperature dataset to match the exact geographical shape of each country. To correct for potential distortions caused by the Earth's curvature on a flat map, we apply a latitude-based weighting. This step is essential for maintaining accuracy, especially in high-latitude regions where distortion is more pronounced. The result of this process is a latitude-weighted average temperature for each nation.

  • It's important to note, however, that due to the resolution constraints of the Copernicus dataset, this methodology might not be as effective for countries with very small landmasses. In these cases, the process may not yield reliable data.

  • The derived 2-meter temperature readings for each country are calculated based on administrative borders, encompassing all land surface types within these defined areas. As a result, temperatures over oceans and seas are not included in these averages, focusing the data primarily on terrestrial environments.

  • Global temperature averages are calculated over all land and ocean surfaces.

  • The temperature anomaly is determined by comparing the average surface temperature of a given month to the 1991-2020 mean, highlighting climate variations.

Reuse this work

  • All data produced by third-party providers and made available by Our World in Data are subject to the license terms from the original providers. Our work would not be possible without the data providers we rely on, so we ask you to always cite them appropriately (see below). This is crucial to allow data providers to continue doing their work, enhancing, maintaining and updating valuable data.
  • All data, visualizations, and code produced by Our World in Data are completely open access under the Creative Commons BY license. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.

Citations

How to cite this page

To cite this page overall, including any descriptions, FAQs or explanations of the data authored by Our World in Data, please use the following citation:

“Data Page: Monthly temperature anomalies”, part of the following publication: Hannah Ritchie, Pablo Rosado and Veronika Samborska (2024) - “Climate Change”. Data adapted from Copernicus Climate Change Service. Retrieved from https://ourworldindata.org/grapher/monthly-temperature-anomalies [online resource]
How to cite this data

In-line citationIf you have limited space (e.g. in data visualizations), you can use this abbreviated in-line citation:

Copernicus Climate Change Service (2019) – with major processing by Our World in Data

Full citation

Copernicus Climate Change Service (2019) – with major processing by Our World in Data. “Monthly temperature anomalies” [dataset]. Copernicus Climate Change Service, “ERA5 monthly averaged data on single levels from 1940 to present 2” [original data]. Retrieved June 26, 2024 from https://ourworldindata.org/grapher/monthly-temperature-anomalies