Hemispheric and global averages graph
(also available as a EPS and PDF)
|
Hemispheric and global averages graph (also available as a EPS and PDF) |
This text gives some brief information to users about the datasets including:
| Dataset | Full grid | End month Updated |
Hemispheric means | Hadley Centre | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| CRUTEM4 | NetCDF (20MB) | 2015-10 2015-11-20 |
| CRUTEM4 | |||||||
| Land air temperature anomalies on a 5° by 5° grid-box basis (Jones et al., 2012) | |||||||||||
| CRUTEM4v | NetCDF (20MB) | 2015-10 2015-11-20 |
| ||||||||
| Variance adjusted version of CRUTEM4 | |||||||||||
| CRUTEM3 | NetCDF (20MB) | 2014-05 2014-06-30 |
| CRUTEM3 | |||||||
| Land air temperature anomalies on a 5° by 5° grid-box basis (superseded by CRUTEM4) | |||||||||||
| CRUTEM3v | NetCDF (20MB) | 2014-05 2014-06-30 |
| ||||||||
| Variance adjusted version of CRUTEM3 (superseded by CRUTEM4v) | |||||||||||
| HadCRUT4 | NetCDF (20MB) | 2015-10 2015-11-20 |
| 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 |
| 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 |
| ||||||||
| Variance adjusted version of HadCRUT3 | |||||||||||
| HadSST3 | NetCDF (20MB) | 2015-11 2015-12-04 |
| HadSST3 | |||||||
| sea surface temperature anomalies from Kennedy et al (2011) | |||||||||||
| HadSST2 | NetCDF (98MB) | 2014-06 2014-07-07 |
| HadSST2 | |||||||
| Sea surface temperature anomalies from Rayner et al (2006) superseded by HadSST3 | |||||||||||
| Absolute | NetCDF (1MB) | ||||||||||
| Absolute temperatures for the base period 1961-90 (see Jones et al., 1999) | |||||||||||
| 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 |
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
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