Global Mean Surface Air Temperature Anomalies from NASA
Goddard Institute for Space Studies(GISS)
- Data Set Overview
- Sponsor
- Original Archive
- Future Updates
- The Data
- Characteristics
- Source
- The Files
- Format
- Name and Directory Information
- Companion Software
- The Science
- Theoretical Basis of Data
- Processing Sequence and Algorithms
- Scientific Potential of Data
- Validation of Data
- Data Access and Contacts
- FTP Site
- Points of Contact
Global mean monthly, seasonal and annual temperature anomalies are given. The anomalies are variations from the means determined for the base period 1951-1980. These data are an update of the analyses described by Hansen and Lebedeff (1987 & 1988). Discussions of the data are given in the references below. The input data for these analyses come from about 2000 meteorological stations around the world, This work was done at the Goddard Institute for Space Studies (GISS) by Dr. James Hansen and his colleagues. Their complete analysis considers regional as well as global mean temperature variations. In the brief summary presented at this site only the global means are given.The production and distribution of this data set are funded by NASA's Earth Science enterprise. The data are not copyrighted; however, we request that when you publish data or results using these data please acknowledge as follows:
The authors wish to thank Dr. James Hansen and his colleagues at the Goddard Institute for Space Studies for the production of this data set, and the Distributed Active Archive Center (Code 902) at the Goddard Space Flight Center, Greenbelt, MD, 20771, for putting these data in their present format and distributing them. These distribution activities were sponsored by NASA's Earth Science enterprise.This data set was constructed by the Surface Air Temperature Study Group at the Goddard Institute for Space Studies. This is also the location of the primary archive and the source for detailed information concerning this data set.
This data set will be updated as new data is made available.
CharacteristicsSource
GISS Temperature Analysis Parameters Mean Global Surface Air Temperature Anomalies Units 0.01 Degrees Celsius Typical Range -130 to +90 (monthly) -60 to +50 (annual) Temporal Coverage January 1866- September 1997 Temporal Resolution: monthly, seasonal and annual means Spatial Coverage Global Spatial Resolution Global The input data for these analyses are principally the Monthly Climatic Data of the World (MCDW) from about 2000 meteorological stations around the world, supplemented for the most recent several months by NOAA near real time data for most of these stations. The MCDW data set is maintained by the National Oceanic and Atmospheric Administration (NOAA) in cooperation with the World Meteorological Organization.
Name and Directory Information Naming ConventionThe GISS Surface Air Temperature Anomalies Global Mean data set consist of a single, 16KB, ASCII-formatted file.
Directory Path
/data/inter_disc/surf_temp_press/tmp_dev/giss/
- Please make note that the data is in a table (ASCII) format
None.
Theoretical Basis of DataThe surface air temperature and the sea surface temperature are basic weather and climate parameters. They are normally measured by thermometers. The present data set was established to examine long term trends; for this reason temperature changes from the 1951-1980 mean are presented rather than the temperatures themselves. A good approximation to the actual annual global-mean temperature is obtained by adding 14 degrees C to the anomalies. The basic problems in examining multidecade temperature trends concern calibration and sampling errors or deficiencies.
Some meteorological stations report above normal warming trends over the last hundred years because cities have grown up around them. The extra heat generated in the city causes urban meteorological stations to have a higher average temperature than rural stations. Since stations tend to cluster in urban regions some have argued that the global warming trend is exaggerated in the existing data. The GISS analysis does not correct for the urban heat island effect, but their analysis shows that the overall effect is small.
Hansen et al. (1995) state:
"Errors in surface air temperature trends due to changes of instrumentation, station location, and diurnal sampling can be substantial at individual locations and require continuing attention (Karl and Williams, 1987). The most serious problem is probably urban heat island effects, which tend to be systematic. Hansen and Lebedeff (1987) found the global warming of the past century in their analysis to be reduced 0.1 degree C when cities of population more than 100,000 were excluded, and they estimated the total global-mean urban effect to be 0.1-0.2 degrees C. A more precise test for the United States, based on comparing rural and MCDW stations, revealed large differences in certain regions such as southern California, but averaged over the contiguous United States the temperature change of MCDW and rural stations differed by only 0.1 degrees C (Hansen et al., 1991)."Processing Sequence and Algorithms
Surface air temperature has been measured at a large number of meteorological stations for over one hundred years, mainly at northern hemisphere land locations. This GISS analysis uses data from about 2000 meteorological stations around the world. The earth's surface is divided into 80 equal area "boxes", the full dimension of a box side being about 2500 km. For the locations of the boxes see Figure 2 in Hansen and Lebedeff (1987). Each of the 80 boxes is subdivided into an array of 10 by 10 equal-area "subboxes". The temperature anomaly for a subbox is defined using all stations located within 1200 km of the subbox's center. Hansen et al. have carried out some quality control of original data by examining, and comparing with nearby stations, those values which differ by more than 5 standard deviations from their long term mean. Undoubtedly some errors remain. They welcome communications from users who find specific errors or unusual behavior in the temperature data, which can help them improve future versions of the dataset. The 100 subbox values in a box are used to find the box average. Latitude zonal, hemispherical and global means are also calculated. Details of the analysis method can be found in Hansen and Lebedeff (1987). The results from subbox to global mean are available from GISS.
The number of available stations and their distribution was much smaller at the start of the time series, 1886, than at present. The GISS analysis procedure is designed to minimize the difficulties this presents in measuring large scale regional and global temperature shifts. The base period, 1951-1980, was chosen because there was reasonably good global coverage available during this period.
GISS has recently also started to produce a shorter combined land and ocean temperature dataset. Better global coverage is obtained by combining meteorological station data with measurements of sea surface temperature (SST). The SST data used by GISS are a blended analysis of satellite and ship measurements by Reynolds and Smith (1994) for the period 1982-present, the satellite providing high resolution while the in situ data provide bias correction. The SST data for 1950-1981 are based on only in situ data (Smith et al. 1996). The land-ocean ltemperature index provides a measure of global temperature change which proves to be in good agreement with the temperature change estimated from the meteorological station network. The land-ocean index has the advantage of providing a more detailed and accurate description of change in marine regions. This data is not available on this Interdisciplinary Data site but it is available from GISS. The East Anglia Temperature Deviations, which are available on this site, also consist of blended land and ocean measurements (Jones et al., 1991).
This dataset can be used for numerous climate studies. Some examples are:
- Global Warming (Houghton et al. 1995; Hansen et al., 1996; Hansen and Lacis, 1990)
- Correlations between various terrestrial climate variables ( Kyle et al., 1995; Ardanuy et al. 1992)
- Correlation of variations in the climate and solar variability (Hoyt and Schatten 1993)
The uncertainty in this data set of the estimated temperature change in a given year is about 0.07C due to just the incomplete sampling of the globe by the station network. Thus the relative rank of different years is uncertain for years whose temperatures differ by less than that amount. However, the GISS land-sea temperature index which uses sea surface temperatures to provide coverage of most ocean areas has a smaller uncertainty. This uncertainty should be kept in mind when comparing the East Anglia and GISS temperature anomalies.
Points of ContactFor information about or assistance in using any DAAC data, contact
EOS Distributed Active Archive Center (DAAC) Code 902
NASA Goddard Space Flight Center
Greenbelt, Maryland 20771
Internet: daacuso@daac.gsfc.nasa.gov
301-614-5224 (voice)
301-614-5268 (fax)Scientists whom you may contact about these data are:
Address: NASA Goddard Institute for Space Studies 2880 Broadway New York, NY 10025
- James Hansen (jhansen@giss.nasa.gov); phone: 212-678-5619
- Reto Ruedy (rruedy@giss.nasa.gov) 212-678-5600
- Makiko Sato (makikosato@giss.nasa.gov) 212-678-5618
Click here to view the NASA Goddard Institute for Space Studies, Surface Air Temperature Analyses Site.
Ardanuy, P.E., H.L. Kyle, and D. Hoyt, 1992: Global relationships between the earth's radiation budget, cloudiness, volcanic aerosols, and surface temperature, J. Climate, 10, 1120-1139
Hansen, J., and S. Lebedeff, 1987: Global trends of measured surface air temperature. J. Geophys. Res. 92, 13,345-13,372.
Hansen, J., and S. Lebedeff. 1988. Global surface air temperatures: Update through 1987. Geophys. Res. Lett. 15, 323-326.
Hansen, J.E., and A. Lacis, 1990: Sun and dust versus greenhouse gases: An assessment of their relative roles in global climate change, Nature, 346, 713-719.
Hansen, J., D. Rind, A. Del Genio, A. Lacis, S. Lebedeff, M. Prather, R. Ruedy, and R. Karl, 1991: Regional greenhouse climate effects, in Greenhouse-Gas_Induced Climatic Change, edited by M. E. Schlesinger, pp 211-229, Elsevier, Amsterdam.
Hansen, J., H. Wilson, M. Sato, R. Ruedy, K. Shah, and E. Hansen. 1995. Satellite and surface temperature data at odds? Climatic Change, 30, 103-117.
Hansen, J., R. Ruedy, M. Sato, and R. Reynolds, 1996: Global surface air temperature in 1995: Return to pre-Pinatubo level. Geophys. Res. Lett. 23, 1665-1668.
Houghton, J.T., L.G. Meira Filho, J. Bruce, H. Lee, B.A. Callander, E. Haites, N. Harris and K. Maskell, Eds. 1995: Climate Change 1994: radiative forcing of climate change and an evaluation of the IPCC IS92 emission scenarios, Cambridge University Press, 339 pp.
Hoyt, D.V. and K.H. Schatten, 1993: A discussion of plausible solar irradiance variations, 1700-1992, J. Geophys. Res., 98, 18895-18906.
Jones, P. D., T. M. L. Wigley, and G. Farmer, 1991: Marine and land temperature data sets: a comparison and a look at recent trends, in, Greenhouse-Gas-induced Climatic Change: A Critical Appraisal of Simulations and Observations, M. E. Schlesinger, Ed., Elsevier Scientific Publishers, New York, 153-172.
Karl, T. R., and C. N. Williams, 1987: An approach to adjusting climatological time series for discontinuous inhomogeneities, J. Clim. Appl. Meteorol., 26, 1744-1763.
Kyle, H.L., M. Weiss and P. Ardanuy, 1995: Cloud, surface temperature, and outgoing longwave radiation for the period from 1979 to 1990, J. Climate, 8, 2644-2658.
Reynolds, R.W., and T.M. Smith. 1994. Improved global sea surface temperature analyses using optimal interpolation. J. Climate 7, 929-948.
Smith, T.M., R.W. Reynolds, R.E. Livezey, and D.C. Stokes. 1996. Reconstruction of historical sea surface temperature using empirical orthogonal functions. J. Climate 9, 1403-1420.
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