Global Surface Temperature Anomalies

Background Information - FAQ

1. What is a temperature anomaly?

   The term temperature anomaly means a departure from a reference value or long-term average. A positive anomaly indicates that the observed temperature was warmer than the reference value, while a negative anomaly indicates that the observed temperature was cooler than the reference value.

2. What can the mean global temperature anomaly be used for?

   This product is a global-scale climate diagnostic tool and provides a big picture overview of average global temperatures compared to a reference value.

3. What datasets are used in calculating the average global temperature anomaly?

   Land surface temperatures are available from the Global Historical Climate Network-Monthly (GHCN-M). Sea surface temperatures are determined using the extended reconstructed sea surface temperature (ERSST) analysis. ERSST uses the most recently available International Comprehensive Ocean-Atmosphere Data Set (ICOADS) and statistical methods that allow stable reconstruction using sparse data. The monthly analysis begins January 1854, but due to very sparse data, no global averages are computed before 1880. With more observations after 1880, the signal is stronger and more consistent over time.

4. What version of the GHCN-M analysis is currently being used?

   Effective June 2015, the GHCN- M version 3.3.0 dataset of monthly mean temperature replaced the GHCN-M version 3.2.2 monthly mean temperature dataset. Beginning with the May 2015 Global monthly State of the Climate Report, released on June 18, 2015, GHCN-M version 3.3.0 is used for NCEI climate monitoring activities, including calculation of global land surface temperature anomalies and trends. For more information about this newest version, please see the GHCN-M version 3.3.0 Technical Report.

5. What version of the ERSST analysis is currently being used?

   ERSST version 4 is currently used. Version 4 of the ERSST dataset improves or corrects for several factors related to sea-surface temperature (SST) measurements. These include: updated and substantially more complete input data from ships and buoys, improved metadata associated with these observations, updated SST quality control procedures, revised SST anomaly evaluation methods, updated corrections to ship SSTs using nighttime marine air temperature, accounting for differences inherent in buoy (relative to ship-borne) observations, and improved methods of identifying and using persistent statistical relationships between neighboring regions to help validate observations and address missing data. Complete information on these changes is available in Huang et al. (2015).

6. When was the use of the ERSST version 4 implemented?

   The transition to the new ERSST version (4) occurred in June 2015. The Climate Monitoring Branch began using the updated merged land-ocean dataset for the May 2015 State of the Climate Report.

7. Why use temperature anomalies (departure from average) and not absolute temperature measurements?

   Absolute estimates of global average surface temperature are difficult to compile for several reasons. Some regions have few temperature measurement stations (e.g., the Sahara Desert) and interpolation must be made over large, data-sparse regions. In mountainous areas, most observations come from the inhabited valleys, so the effect of elevation on a region's average temperature must be considered as well. For example, a summer month over an area may be cooler than average, both at a mountain top and in a nearby valley, but the absolute temperatures will be quite different at the two locations. The use of anomalies in this case will show that temperatures for both locations were below average.

   Using reference values computed on smaller [more local] scales over the same time period establishes a baseline from which anomalies are calculated. This effectively normalizes the data so they can be compared and combined to more accurately represent temperature patterns with respect to what is normal for different places within a region.

   For these reasons, large-area summaries incorporate anomalies, not the temperature itself. Anomalies more accurately describe climate variability over larger areas than absolute temperatures do, and they give a frame of reference that allows more meaningful comparisons between locations and more accurate calculations of temperature trends.

8. How is the average global temperature anomaly time-series calculated?

   The global time series is produced from the Smith and Reynolds blended land and ocean data set (Smith et al., 2008). This data set consists of monthly average temperature anomalies on a 5° x 5° grid across land and ocean surfaces. These grid boxes are then averaged to provide an average global temperature anomaly. An area-weighted scheme is used to reflect the reality that the boxes are smaller near the poles and larger near the equator. Global-average anomalies are calculated on a monthly and annual time scale. Average temperature anomalies are also available for land and ocean surfaces separately, and the Northern and Southern Hemispheres separately. The global and hemispheric anomalies are provided with respect to the period 1901-2000, the 20^th century average.

9. Why do some of the products use different reference periods?

   The national maps show temperature anomalies relative to the 1981–2010 base period. This period is used in order to comply with a recommended World Meteorological Organization (WMO) Policy, which suggests using the latest decade for the 30-year average. For the global-scale averages (global land and ocean, land-only, ocean-only, and hemispheric time series), the reference period is adjusted to the 20th Century average for conceptual simplicity (the period is more familiar to more people, and establishes a longer-term average). The adjustment does not change the shape of the time series or affect the trends within it.

10. How often and when is the global average temperature dataset updated?

    The dataset is updated every month. Data for a month are typically made available by the 15th of the following month.

11. What is the difference between the gridded dataset and the index values?

    The land and ocean gridded dataset is a large file (~24 mb) that contains monthly temperature anomalies across the globe on a 5 deg x 5 deg grid. The anomalies are calculated with respect to the 1981–2010 base period. Gridded data is available for every month from January 1880 to the most recent month available. You can use it to examine anomalies in different regions of the earth on a month-by-month basis. The index values are an average of the gridded values (see question #7); however, the anomalies are provided with respect to the 20^th century (1901–2000) average. They are most useful for tracking the big-picture evolution of temperatures across larger parts of the planet, up to and including the entire global surface temperature.

Global Mean Monthly Surface Temperature Estimates for the Base Period 1901 to 2000

Gridded Dataset

To obtain a copy of the monthly average temperature anomalies on a 5° x 5° grid across land and ocean surfaces, please use the following information to access anonymous FTP at NCEI:

Machine Address: ftp.ncdc.noaa.gov
Login Name: anonymous
Password: your email address
Directory: /pub/data/ghcn/blended/
Enter: bin
Enter: get ncdc-merged-sfc-mntp.dat.gz

For information on the format of the dataset, please visit our Global Historical Climatology Network page. A direct link to the file is also provided on the website.

The Global Anomalies and Index Data

Annual time series are not available for Northern/Southern Hemisphere (only Global). Anomalies are with respect to the 20^th century average (1901-2000). Monthly and annual global anomalies are available through the most recent complete month and year, respectively.

Additional Websites

• NCEI Extended Reconstructed SST Datasets
• About ERSST Version 4
• State of Climate - Global Analysis

References

• Peterson, T. C., and R. S. Vose (1997), An Overview of the Global Historical Climatology Network Temperature Database, Bull. Am. Meteorol. Soc., 78, 2837-2849.
• Quayle, R.G., T.C. Peterson, A.N. Basist, and C.S. Godfrey, 1999: An operational near-real-time global temperature index. Geophys. Res. Lett.. 26, 3 (Feb. 1, 1999), 333-335.
• Smith, T. M., and R. W. Reynolds (2004), Improved extended reconstruction of SST (1854-1997), J. Climate, 17, 2466-2477.
• Smith, T. M., and R. W. Reynolds (2005), A global merged land air and sea surface temperature reconstruction based on historical observations (1880-1997), J. Climate, 18, 2021-2036.
• Smith, T. M., et al. (2008), Improvements to NOAA's Historical Merged Land-Ocean Surface Temperature Analysis (1880-2006), J. Climate, 21, 2283-2293.
• Boyin Huang, Viva F. Banzon, Eric Freeman, Jay Lawrimore, Wei Liu, Thomas C. Peterson, Thomas M. Smith, Peter W. Thorne, Scott D. Woodruff, and Huai-Min Zhang, 2015: Extended Reconstructed Sea Surface Temperature Version 4 (ERSST.v4). Part I: Upgrades and Intercomparisons. J. Climate, 28, 911–930.

The complete land-sea surface climatology from the Climate Research Unit is described in:

• Jones, P. D., M. New, D. E. Parker, S. Martin, and I. G. Rigor (1999), Surface Air Temperature and its Changes Over the Past 150 Years, Rev. Geophys., 37(2), 173—199.

Global land areas, excluding Antarctica, described in:

• New, M. G., M. Hulme and P. D. Jones, in press: Representing 20th century space-time climate variability. I: Development of a 1961-1990 mean monthly terrestrial climatology. J. Climate.

Global oceans, 60S-60N, described in:

• Parker, D. E., M. Jackson and E. B. Horton, 1995: The GISST2.2 sea surface temperature and sea-ice climatology. Climate Research Technical Note, CRTN 63, Hadley Centre for Climate Prediction and Research, Bracknel, UK.

Arctic sea areas, described in:

• Rigor, I. G., R. L. Colony and S. Martin, submitted: Statistics of surface air temperature observations in the Arctic. J. Climate.
• Martin, S. and E.A. Munoz: Properties of the Arctic 2-Meter Air temperature field for 1979 to the present derived from a new gridded data set. J. Climate, 10, 1428-1440.