Climatic Research Unit : Data
Graph
Hemispheric and global averages graph
(also available as a EPS and PDF)

Temperature

These datasets have been developed by the Climatic Research Unit (University of East Anglia) in conjunction with the Hadley Centre (at the UK Met Office), apart from the two SST datasets which were developed solely by the Hadley Centre. These datasets will be updated at roughly monthly intervals into the future. Hemispheric and global averages as monthly and annual values are available as separate files.

This text gives some brief information to users about the datasets including:

Data for Downloading

Dataset Full grid End month
Updated
Hemispheric means Hadley Centre
CRUTEM4 
NetCDF
(20MB)
 
2014-05
2014-06-30
NHCRUTEM4
SH
GL
CRUTEM4
Land air temperature anomalies on a 5 by 5 grid-box basis (Jones et al., 2012)
CRUTEM4v 
NetCDF
(20MB)
 
2014-05
2014-06-30
NHCRUTEM4v
SH
GL
Variance adjusted version of CRUTEM4
CRUTEM3 
NetCDF
(20MB)
 
2014-05
2014-06-30
NHCRUTEM3
SH
GL
CRUTEM3
Land air temperature anomalies on a 5 by 5 grid-box basis (to be superceded by CRUTEM4)
CRUTEM3v 
NetCDF
(20MB)
 
2014-05
2014-06-30
NHCRUTEM3v
SH
GL
Variance adjusted version of CRUTEM3 (to be superceded by CRUTEM4v)
HadCRUT4 
NetCDF
(20MB)
 
2014-05
2014-06-27
NHHadCRUT4
SH
GL
HadCRUT4
Combined land [CRUTEM4] and marine [sea surface temperature (SST) anomalies from HadSST3, see Kennedy et al., 2011] temperature anomalies on a 5 by 5 grid-box basis
HadCRUT3 
NetCDF
(20MB)
 
2014-05
2014-06-26
NHHadCRUT3
SH
GL
HadCRUT3
Combined land [CRUTEM3] and marine [sea surface temperature (SST) anomalies from HadSST2, see Rayner et al., 2006] temperature anomalies on a 5 by 5 grid-box basis
HadCRUT3v 
NetCDF
(20MB)
 
2014-05
2014-06-26
NHHadCRUT3v
SH
GL
Variance adjusted version of HadCRUT3
HadSST3 
NetCDF
(20MB)
 
2014-05
2014-06-05
NHHadSST3
SH
GL
HadSST3
sea surface temperature anomalies from Kennedy et al (2011)
HadSST2 
NetCDF
(98MB)
 
2013-08
2013-09-18
NHHadSST2
SH
GL
HadSST2
Sea surface temperature anomalies from Rayner et al (2006)
Absolute 
NetCDF
(1MB)
Absolute temperatures for the base period 1961-90 (see Jones et al., 1999)

File Formats

NetCDF is widely supported by open-source software such as R, Panoply and commercial packages such as Matlab, IDL. The CRUTEM4 data are also available via a Google Earth interface.

Hemispheric/global average data file format

 for year = 1850 to endyear
  format(i5,13f7.3) year, 12 * monthly values, annual value
  format(i5,12i7) year, 12 * percentage coverage of hemisphere or globe 
Coverage of 0 means data not yet available
Download an R function to read this format

References

Answers to Frequently-asked Questions

The answers given are intended to be brief rather than comprehensive. For complete details readers are referred to the scientific references already given.

What is the updating schedule?

All the grid-related files on this page (except Absolute) will be updated on a monthly basis to include the latest month within about four weeks of its completion. Updating includes not just data for the last month but the addition of any late reports for up to approximately the last two years.

Every year, we will add in updated data for stations that do not report in real time using stations we will be accessing from National Meteorological Services (NMSs) around the world. This addition will take place around the second month of the year, as by then sufficient NMSs should have made their monthly average data available for the preceding year. Where available, we will add in extra data from some NMSs when they make more homogeneous data available. The routine annual updates will include data from the USA, Canada, Russia, Australia and a number of European countries. When this annual update is complete, we will update the station data at this time.

How are the hemispheric and global anomaly series calculated?

Values for the hemisphere are the weighted average of all the non-missing, grid-box anomalies in each hemisphere. The weights used are the cosines of the central latitudes of each grid box. The global average for CRUTEM4 and CRUTEM4v is a weighted average of the Northern Hemisphere (NH) and Southern Hemisphere (SH). The weights are 2 for the NH and one for the SH. For CRUTEM3 and CRUTEM3v, the global average is the average of the NH and SH values. For HadCRUT4, HadCRUT3 and HadCRUT3v the global average is the average of the NH and SH values. In the time series files, the second row of integers is the percentage of the surface area covered for each month from 1850.

What are the basic raw data used?

For land regions of the world over 4800 monthly station temperature time series are used. Coverage is denser over the more populated parts of the world, particularly, the United States, southern Canada, Europe and Japan. Coverage is sparsest over the interior of the South American and African continents and over Antarctica. The number of available stations was small during the 1850s, but increases to over 4500 stations during the 1951-2010 period. For marine regions sea surface temperature (SST) measurements taken on board merchant and some naval vessels are used. As the majority come from the voluntary observing fleet, coverage is reduced away from the main shipping lanes and is minimal over the Southern Oceans. Improvements in coverage occur after 1980 through the deployment of fixed and drifting buoys. The development of the datasets is extensively discussed in Jones et al. (2012) and Kennedy et al. (2011). Both these sources also extensively discuss the issue of consistency and homogeneity of the measurements through time and the steps that have been made to remove non-climatic inhomogeneities.

Raw station data are available from the Met Office website for both CRUTEM4 and CRUTEM3. For CRUTEM4, the station data (and graphs) are also available via a Google Earth interface.

Why are sea surface temperatures rather than air temperatures used over the oceans?

Over the ocean areas the most plentiful and most consistent measurements of temperature have been taken of the sea surface. Marine air temperatures (MAT) are also taken and would, ideally, be preferable when combining with land temperatures, but they involve more complex problems with homogeneity than SSTs (Kennedy et al., 2011). The problems are reduced using night only marine air temperature (NMAT) but at the expense of discarding approximately half the MAT data. Our use of SST anomalies implies that we are tacitly assuming that the anomalies of SST are in agreement with those of MAT. Kennedy et al. (2011) provide comparisons of hemispheric and large area averages of SST and NMAT anomalies.

Why are the temperatures expressed as anomalies from 1961-90?

Stations on land are at different elevations, and different countries measure average monthly temperatures using different methods and formulae. To avoid biases that could result from these problems, monthly average temperatures are reduced to anomalies from the period with best coverage (1961-90). For stations to be used, an estimate of the base period average must be calculated. Because many stations do not have complete records for the 1961-90 period several methods have been developed to estimate 1961-90 averages from neighbouring records or using other sources of data (see more discussion on this and other points in Jones et al., 2012). Over the oceans, where observations are generally made from mobile platforms, it is impossible to assemble long series of actual temperatures for fixed points. However it is possible to interpolate historical data to create spatially complete reference climatologies (averages for 1961-90) so that individual observations can be compared with a local normal for the given day of the year (more discussion in Kennedy et al., 2011).

It is possible to develop an absolute temperature series for any area selected, using the absolute file, and then add this to a regional average in anomalies calculated from the gridded data. If for example a regional average is required, users should calculate a time series in anomalies, then average the absolute file for the same region then add the average derived to each of the values in the time series. Do NOT add the absolute values to every grid box in each monthly field and then calculate large-scale averages.

Why do anomalies not average exactly zero over 1961-90?

Over both the land and marine domains considerable care has been taken in calculating the base period values for the 1961-90 period (see Jones et al., 2012). However, as all regions don't have complete data for this 30-year period, the anomaly data do not average exactly to zero for this 30-year period. This applies to the global and hemispheric average series as well as the individual grid-box series.

How are the land and marine data combined?

Both the component parts (land and marine) are separately averaged into the same 5 x 5 latitude/longitude grid boxes. The combined version (HadCRUT4 ) takes values from each component and weights the grid boxes according to the area, ensuring that the land component has a weight of at least 25% for any grid box containing some land data. The weighting method is described in detail in Morice et al. (2012). The previous combined versions (HadCRUT3 and HadCRUT3v) take values from each component and weight the grid boxes where both occur (coastlines and islands) according their errors of estimate (see Brohan et al., 2006 for details).

How accurate are the hemispheric and global averages?

Uncertainty estimates are supplied with the same data given at the Met Office site: CRUTEM4, CRUTEM3, HadCRUT4, HadCRUT3.

Why can I not exactly reproduce the hemispheric and global averages for HadCRUT4 and HadSST3 that are given here?

Both these are ensemble datasets. This means that there are 100 realizations of each in order to calculate the possible assumptions involved in the structure of the various components of the error (see discussion in Morice et al., 2012). All 100 realizations are available at the above Met Office site, but we have selected here the ensemble median. For the gridded data this is the ensemble median calculated separately for each grid box for each time step from the 100 members. For the hemispheric and global averages this is again the median of the 100 realizations. The median of the gridded series will not produce the median of the hemispheric and global averages, but the differences will be small.

Why are values slightly different when I download an updated file a year later?

All the files on this page (except Absolute) will be updated on a monthly basis to include the latest month within about four weeks of its completion. Updating includes not just data for the last month but the addition of any late reports for up to approximately the last two years. Every year, we will also add in updated data for stations that do not report in real time using station we will be accessing from National Meteorological Services (NMSs) around the world. This addition will take place around the second month of the year, as by then sufficient NMSs will have made their monthly average data available for the preceding year. Where available, we will add in extra data from some NMSs when they make more homogeneous data available. The routine annual updates will include data from the USA, Canada, Russia, Australia and a number of European countries.

In addition to this the method of variance adjustment (used for CRUTEM3v, CRUTEM4v and HadCRUT3v) works on the anomalous temperatures relative to the underlying trend on an approximate 30-year timescale. Estimating this trend requires estimation of grid-box temperatures for years before the start of each record and after the end. With the addition of subsequent years, the underlying trend will alter slightly, changing the variance-adjusted values. Effects will be greatest on the last year of the record, but an influence can be evident for the last three to four years. Full details of the variance adjustment procedure are given in Jones et al. (2001).

See also


Last updated: March 2014, Phil Jones, Colin Harpham, Tim Osborn & Mike Salmon (plus automatic updates for data)

These datasets are made available under the Open Database License.
Any rights in individual contents of the datasets are licensed under the
Database Contents License under the conditions of Attribution and Share-Alike.
Please use the attribution Climatic Research Unit, University of East Anglia