| Global Maps |
| Station Data |
| Animations |
| Time Series of Zonal Means |
| Seasonal Cycle of Zonal Means|
2008-06-07: Various insignificant changes to analysis, see "Updates to Analysis" below.
2008-03-01: USHCN data now taken from NOAA's ftp site rather than from CDIAC website. For more, see "Updates to Analysis" below.
The basic GISS temperature analysis scheme was defined in the late 1970s by James Hansen when a method of estimating global temperature change was needed for comparison with one-dimensional global climate models. Prior temperature analyses, most notably those of Murray Mitchell, covered only 20-90°N latitudes. Our rationale was that the number of Southern Hemisphere stations was sufficient for a meaningful estimate of global temperature change, because temperature anomalies and trends are highly correlated over substantial geographical distances. Our first published results (Hansen et al. 1981) showed that, contrary to impressions from northern latitudes, global cooling after 1940 was small, and there was net global warming of about 0.4°C between the 1880s and 1970s.
The analysis method was documented in Hansen and Lebedeff (1987), showing that the correlation of temperature change was reasonably strong for stations separated by up to 1200 km, especially at middle and high latitudes. They obtained quantitative estimates of the error in annual and 5-year mean temperature change by sampling at station locations a spatially complete data set of a long run of a global climate model, which was shown to have realistic spatial and temporal variability.
This derived error bar only addressed the error due to incomplete spatial coverage of measurements. As there are other potential sources of error, such as urban warming near meteorological stations, etc., many other methods have been used to verify the approximate magnitude of inferred global warming. These methods include inference of surface temperature change from vertical temperature profiles in the ground (bore holes) at many sites around the world, rate of glacier retreat at many locations, and studies by several groups of the effect of urban and other local human influences on the global temperature record. All of these yield consistent estimates of the approximate magnitude of global warming, which has now increased to about twice the magnitude that we reported in 1981. Still further affirmation of the reality of the warming is its spatial distribution, which shows largest values at locations remote from any local human influence, with a global pattern consistent with that expected for response to global climate forcings (larger in the Northern Hemisphere than the Southern Hemisphere, larger at high latitudes than low latitudes, larger over land than over ocean).
Some improvements in the analysis were made several years ago (Hansen et al. 1999; Hansen et al. 2001), including use of satellite-observed night lights to determine which stations in the United States are located in urban and peri-urban areas, the long-term trends of those stations being adjusted to agree with long-term trends of nearby rural stations.
Current Analysis Method
The current analysis uses surface air temperatures measurements from the following data sets: the unadjusted data of the Global Historical Climatology Network (Peterson and Vose, 1997 and 1998), United States Historical Climatology Network (USHCN) records through 2005, and SCAR (Scientific Committee on Antarctic Research) data from Antarctic stations. The basic analysis method is described by Hansen et al. (1999), with several modifications described by Hansen et al. (2001) also included. The GISS analysis is updated monthly.
The GHCN/USHCN/SCAR data are modified in two steps to obtain station data from which our tables, graphs, and maps are constructed. In step 1, if there are multiple records at a given location, these are combined into one record; in step 2, the urban and peri-urban (i.e., other than rural) stations are adjusted so that their long-term trend matches that of the mean of neighboring rural stations. Urban stations without nearby rural stations are dropped.
A global temperature index, as described by Hansen et al. (1996), is obtained by combining the meteorological station measurements with sea surface temperatures based in early years on ship measurements and in recent decades on satellite measurements. Uses of this data should credit the original sources, specifically the British HadISST group (Rayner and others) and the NOAA satellite analysis group (Reynolds, Smith and others). (See references.)
The analysis is limited to the period since 1880 because of poor spatial coverage of stations and decreasing data quality prior to that time. Meteorological station data provide a useful indication of temperature change in the Northern Hemisphere extratropics for a few decades prior to 1880, and there are a small number of station record s that extend back to previous centuries. However, we believe that analyses for these earlier years need to be carried out on a station by station basis with an attempt to discern the method and reliability of measurements at each station, a task beyond the scope of our analysis. Global studies of still earlier times depend upon incorporation of proxy measures of temperature change. References to such studies are provided in Hansen et al. (1999).
Programs used in the GISTEMP analysis and documentation on their use are available for download. The programs assume a Unix-like operating system and require familiarity with FORTRAN, C and Python for installation.
Updates to Analysis
Graphs and tables are updated around the 10th of every month using the current GHCN and SCAR files. The new files incorporate reports for the previous month and late reports and corrections for earlier months. NOAA updates the USHCN data at a slower, less regular frequency. We will switch to a later version, as soon as a new complete year is available.
Several minor updates to the analysis have been made since its last published description by Hansen et al. (2001). After a testing period they were incorporated at the time of the next routine update. The only change having a detectable influence on analyzed temperature was the 7 August 2007 change to correct a discontinuity in 2000 at many stations in the United States. This flaw affected temperatures in 2000 and later years by ~0.15°C averaged over the United States and ~0.003°C on global average. Contrary to reports in the media, this minor flaw did not alter the years of record temperature, as shown by comparison here of results with the data flaw ('old analysis') and with the correction ('new analysis').
August 2003:A longer version of Hohenpeissenberg station data was made available to GISS and added to the GHCN record. This had no noticeable impact on the global analyses.
March 2005:SCAR data were added to the analysis. This increased data coverage over Antarctica, as evident in the global maps of temperature anomalies.
April 2006:HadISST ocean temperatures are now used only for regions that are identified as ice-free in both the NOAA and HadISST records. This change effects a small number of gridboxes in which HadISST has sea ice while NOAA has open water. The prior approach damped temperature change at these gridboxes because of specification of a fixed temperature in sea ice regions. The new approach still yields a conservative estimate of surface air temperature change, as surface air temperature usually changes markedly when sea ice is replaced by open water or vice versa. Because of the small area of these gridboxes the effect on global temperature change was negligible.
August 7, 2007:A discontinuity in station records in the U.S. was discovered and corrected (GHCN data for 2000 and later years were inadvertently appended to USHCN data for prior years without including the adjustments at these stations that had been defined by the NOAA National Climate Data Center). This had a small impact on the U.S. average temperature, about 0.15°C, for 2000 and later years, and a negligible effect on global temperature, as is shown here.
This August 2007 change received international attention via discussions on various blogs and repetition by some other media, with no graphs provided to show the insignificance of the effect. Further discussions of the curious misinformation are provided by Dr. Hansen on his personal webpage (e.g., his post on "The Real Deal: Usufruct & the Gorilla").
September 10, 2007: The year 2000 version of USHCN data was replaced by the current version (with data through 2005). In this newer version, NOAA removed or corrected a number of station records before year 2000. Since these changes included most of the records that failed our quality control checks, we no longer remove any USHCN records. The effect of station removal on analyzed global temperature is very small, as shown by graphs and maps available here.
March 1, 2008: Starting with our next update, USHCN data will be taken from NOAA's ftp site -- the original source for that file -- rather than from CDIAC's web site; this way we get the most recent publicly available version. Whereas CDIAC's copy currently ends in 12/2005, NOAA's file extends through 5/2007. Note: New updates usually also include changes to data from previous years. Whereas the GHCN and SCAR data are updated every month, updates to the USHCN data occur at irregular intervals.
The publicly available source codes were modified to automatically adjust if new years are added.
June 9, 2008: Effective June 9, 2008, our analysis moved from a 15-year-old machine (soon to be decommissioned) to a newer machine; this will affect some results, though insignificantly. Some sorting routines were modified to minimize such machine dependence in the future. In addition, a typo was discovered and corrected in the program that dealt with a potential discontinuity in the Lihue station record. Finally, some errors were noticed on http://www.antarctica.ac.uk/met/READER/temperature.html (set of stations not included in Met READER) that were not present before 8/2007. We replaced those outliers with the originally reported values. Those two changes had about the same impact on the results than switching machines (in each case the 1880-2007 change was affected by 0.002°C). See graph and maps.
Table Data: Global and Zonal Mean Anomalies dTs
Plain text files in tabular format of temperature anomalies. Anomaly values indicate the difference from the corresponding 1951-1980 means.
- Global-mean monthly, annual and seasonal dTs based on met.station data, 1880-present, updated through most recent month
- Northern Hemisphere-mean monthly, annual and seasonal dTs based on met.station data, 1880-present, updated through most recent month
- Southern Hemisphere-mean monthly, annual and seasonal dTs based on met.station data, 1880-present, updated through most recent month
- Global-mean monthly, annual and seasonal land-ocean temperature index, 1880-present, updated through most recent month
- Zonal-mean annual dTs, 1880-present, updated through most recent complete calendar year
- Zonal-mean annual land-ocean temperature index, 1880-present, updated through most recent completed year
Gridded Monthly Maps of Temperature Anomaly Data
Users interested in the entire gridded temperature anomaly data may download the three basic binary files from our ftp site. Also available there are various FORTRAN programs and instructions to create (time series of) regular gridded anomaly maps from these files. This should make the maintenance of the files mentioned below unnecessary.
Data files for individual years may be obtained from the ftp site's subdirectories: bin for binary format, txt for ASCII text, and netcdf for netCDF.
These files will no longer be updated; they will eventually be removed from this site.
Anomalies and Absolute Temperatures
Our analysis concerns only temperature anomalies, not absolute temperature. Temperature anomalies are computed relative to the base period 1951-1980. The reason to work with anomalies, rather than absolute temperature is that absolute temperature varies markedly in short distances, while monthly or annual temperature anomalies are representative of a much larger region. Indeed, we have shown (Hansen and Lebedeff, 1987) that temperature anomalies are strongly correlated out to distances of the order of 1000 km. For a more detailed discussion, see The Elusive Absolute Surface Air Temperature.
Please see the GISTEMP references page for more citations to publications related to this research.
Please address scientific inquiries about the GISTEMP analysis to Dr. James Hansen.
Please address technical questions about these GISTEMP webpages to Dr. Reto Ruedy.