Near-surface temperature over land
Information about CRUTEM4: the land component of the global surface temperature dataset HadCRUT4.
CRUTEM4 is a dataset of maps of anomalies - the temperature relative to the average during a reference period - in near-surface land temperature for each month since January 1850. It is updated monthly with new data. This page covers the following topics:
CRUTEM4 is based on an archive of monthly mean temperatures provided by more than 5000 weather stations distributed around the world. At present there are around 1300 monitoring stations that regularly contribute to CRUTEM4. The observation stations are operated by national meteorological services.
Each monthly average station temperature is converted to an anomaly by calculating the difference from the 1961 to 1990 average temperature of that month for that station. These anomaly data are then averaged onto a grid that divides the earth's surface into 'squares' that are all 5° in size in latitude and longitude. The value for each grid square is the average of all the station anomalies within it. The resulting map of gridded station anomalies is a global map of temperature differences from the 1961-1990 average for that month over land.
Each month, temperature measurements received from the monitoring stations are checked both by computer and manually to find and remove errors. Over longer time scales, errors in temperature records resulting from changes in measurement practice are identified and removed in a process that is known as homogenisation. While every effort is made to detect and remove errors in the data, scientists accept that with such a large quantity of information not every problem can be found. There is also some uncertainty involved with calculating an average that represents an area using temperature measurements made at discrete locations.
In order to understand the effect of these uncertainties, estimates are made of:
- uncertainties related to the accuracy of day-to-day temperature measurements;
- uncertainties with effects that can persist over a number of years such as uncertainties associated with changes in measurement practice;
- large-scale systematic effects such as due to the growth of urban areas;
- sampling uncertainties caused by the limited number of measurements available to calculate the averages over the 5º grid cells.
These are combined to tell us the overall uncertainty in the temperature anomaly value for each CRUTEM4 grid cell. They are also used to calculate the uncertainty in larger scale averages such as the average land temperature across the globe.
The image at the top right shows a map of anomalies in temperature near the land surface in the most recent month of the CRUTEM4 dataset. The anomalies shown are the difference between the actual temperature and the average in the same month during a reference period of 1961 to 1990, so the map is showing where the land is currently cooler or warmer than it was during this reference period. See our Climate bulletins for information about what has happened in the climate of this month.
The data for each month of CRUTEM4 can be averaged together to give an estimate of the global average near-surface temperature anomaly for each year of the dataset. The results are shown in the time series plot on the right. The plot shows our best estimates of the annual average temperature anomaly for each year (black line) and the 95% confidence range for each (grey area). This confidence range represents how much certainty we have in the values. In 19 out 20 cases we expect the true value of the annual average temperature anomaly to be within this range.
Our page about Global surface temperature has discussion about features that can be seen in time series of near-surface temperature and also shows how changes in the temperature of the sea surface compares to the land.
A complete description of the dataset can be found in the following papers (a version of which is available at the link above):
Jones, P. D., D. H. Lister, T. J. Osborn, C. Harpham, M. Salmon, and C. P. Morice (2012), Hemispheric and large-scale land surface air temperature variations: An extensive revision and an update to 2010, J. Geophys. Res., 117, D05127, doi:10.1029/2011JD017139
P. Brohan, J.J. Kennedy, I. Harris, S.F.B. Tett and P.D. Jones, Uncertainty estimates in regional and global observed temperature changes: a new dataset from 1850. J. Geophys. Res, 111, D12106, doi:10.1029/2005JD006548.