Temperature measurements made over land and in water across the globe are used to tell us how temperature has changed over time and how temperatures vary from region to region.
Shown on the right is a map of the temperature anomalies in the most recent month of HadCRUT4 - a global surface temperature dataset produced by the Met Office Hadley Centre in collaboration with the University of East Anglia Climatic Research Unit (CRU) . The anomalies are the difference between the current temperature and the average for this month in data recorded from 1961 to 1990, so the map is showing whether each part of the globe is currently warmer or cooler than this reference period. Gaps in the map occur where there were no observations.
In most months coherent areas of above and below average temperature can be seen, often extending for thousands of kilometres. See our climate bulletins for descriptions of the main features of the surface temperatures in this month.
The data shown in the map can be used to calculate the global average temperature anomaly. The same can be done with the data from the other months of the dataset to give a time series that shows how global average temperature has changed over time.
The plot on the right shows annual global average temperature anomalies from HadCRUT4 (black line). These represent our best estimates of global surface temperature. However, because there are difficulties in the calculation, the true value of global surface temperature might differ from these best estimates. We put a lot of effort into understanding how much these could affect the numbers and the result is the gray shading in the plot. We expect that the true value of the global temperature will fall within that shaded area in 19 out of 20 cases.
Other groups also produce surface temperature datasets, for example the National Aeronautics and Space Administration Goddard Institute for Space Studies (NASA GISS) and the National Oceanic and Atmospheric Administration National Climatic Data Center (NOAA NCDC). These are produced separately to HadCRUT4 and use different methodologies (see How is HadCRUT4 produced? for more details). Global average temperature anomalies from these datasets are also shown in the plot (orange and blue lines respectively). Although many of the features of the time series are very similar, the global average temperature calculated from each are not exactly the same owing to the differences in the methodologies used.
Global average land and sea surface temperatures have increased since 1850, thought to be caused by:
Overlaid on the longer term changes in global temperature are faster fluctuations. Examples of these are:
It is possible to calculate global averages of temperatures over land, over the ocean or using all the data.
The plots on the right show sea, land and combined annual average temperature anomaly times series (black lines) and the 95% confidence ranges in the values (gray areas; as with the previous time series we expect the true value to fall within this range in 19 out of 20 cases).
HadCRUT4 is made up of observations recorded over more than 160 years and is extended up to the present each month with the new data that have been recorded.
Tens of thousands of temperature observations are taken across the globe each day, both on land and at sea.
These observations are combined with archives of historical observations that have been gathered over the past 160 years. The historical data are adjusted to minimise the effects of changes in the way measurements were made.
The data are first converted into 'anomalies'. Anomalies are the difference in temperature from the 'normal' level. For HadCRUT4 the 'normal' level is the long term average for each area over 1961 to 1990.
Anomalies are used because:
The anomalies are aggregated onto a regular grid that divides the earth's surface into 'squares' that are equally spaced in latitude and longitude. Global average temperature is calculated from these grid squares. The squares do not all have the same area, but this is taken into account in the calculation.
There are often large areas from which we receive few, or no, observations. If no observations were made in a grid square in a month then that grid square is not included in the average. The gaps where there are no observations are largest at high latitudes. We calculate an uncertainty estimate which indicates how far from the 'true' global average our estimate is likely to be.
The graphic showing global average temperature anomalies from the HadCRUT4, NASA GISS and NOAA NCDC datasets reveals that there are some differences between them. One of the main reasons for this is in how each group treats the gaps in the data.
The table shows global average temperature anomalies for the past 26 years from the three major global temperature datasets. Anomalies have been calculated relative to the average for the period 1961-1990. Original data sources can be found at these locations: HadCRUT4, NASA GISS and NOAA NCDC. More information about the HadCRUT3 data can be found on the HadCRUT4 page.
|Year||HadCRUT4 in°C (95% confidence range)||HadCRUT3 in °C (95% confidence range)||NCDC in °C||GISS in °C|
|2015||0.75 (0.66 to 0.84)||0.78||0.76|
|2014||0.57 (0.48 to 0.66)||0.62||0.64|
|2013||0.50 (0.41 to 0.59)||0.46 (0.36 to 0.55)||0.54||0.55|
|2012||0.47 (0.38 to 0.56)||0.40 (0.30 to 0.49)|
|2011||0.42 (0.33 to 0.51)||0.35 ( 0.25 to 0.44)||0.45||0.50|
|2010||0.56 (0.47 to 0.65)||0.50 ( 0.40 to 0.59)||0.58||0.62|
|2009||0.51 (0.42 to 0.60)||0.44 ( 0.34 to 0.54)||0.51||0.54|
|2008||0.40 (0.31 to 0.48)||0.31 ( 0.21 to 0.41)||0.42||0.44|
|2007||0.49 (0.41 to 0.58)||0.40 ( 0.30 to 0.50)||0.49||0.56|
|2006||0.51 (0.42 to 0.60)||0.43 ( 0.33 to 0.53)||0.49||0.53|
|2005||0.54 (0.45 to 0.64)||0.47 ( 0.37 to 0.58)||0.54||0.59|
|2004||0.45 (0.36 to 0.54)||0.43 ( 0.33 to 0.53)||0.45||0.44|
|2003||0.51 (0.42 to 0.60)||0.46 ( 0.36 to 0.56)||0.49||0.51|
|2002||0.50 (0.41 to 0.59)||0.46 ( 0.36 to 0.55)||0.48||0.53|
|2001||0.44 (0.35 to 0.53)||0.40 ( 0.30 to 0.50)||0.42||0.44|
|2000||0.30 (0.21 to 0.39)||0.24 ( 0.14 to 0.33)||0.30||0.32|
|1999||0.31 (0.22 to 0.40)||0.26 ( 0.17 to 0.36)||0.32||0.32|
|1998||0.54 (0.45 to 0.62)||0.52 ( 0.42 to 0.61)||0.51||0.53|
|1997||0.39 (0.30 to 0.48)||0.36 ( 0.26 to 0.45)||0.39||0.38|
|1996||0.18 (0.09 to 0.27)||0.12 ( 0.03 to 0.22)||0.20||0.24|
|1995||0.32 (0.23 to 0.41)||0.28 ( 0.18 to 0.37)||0.33||0.36|
|1994||0.21 (0.12 to 0.30)||0.17 ( 0.07 to 0.26)||0.22||0.22|
|1993||0.15 (0.06 to 0.23)||0.10 ( 0.01 to 0.19)||0.16||0.14|
|1992||0.11 (0.02 to 0.19)||0.06 (-0.03 to 0.15)||0.13||0.13|
|1991||0.25 (0.17 to 0.34)||0.20 ( 0.11 to 0.29)||0.28||0.32|
Last updated: 13 May 2016