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National Oceanic and Atmospheric Administration Global Surface Temperature Anomalies

National Climatic Data Center, Asheville, NC
January 10, 2000

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Overview An Operational Near Real Time Global Temperature Index Global Long-term Mean Land and Sea Surface Temperatures The Global Anomalies and Index

Overview

NCDC's long-term mean temperatures for the Earth were calculated by processing data from thousands of world-wide observation sites on land and sea for the entire period of record of the data. Many parts of the globe are inaccessible and therefore have no data. The temperature anomaly time series presented here were calculated in a way that did not require knowing the actual mean temperature of the Earth in these inaccessible areas such as mountain tops and remote parts of the Sahara Desert where there are no regularly reporting weather stations. Using the collected data available, the whole Earth long-term mean temperatures were calculated by interpolating over uninhabited deserts, inaccessible Antarctic mountains, etc. in a manner that takes into account factors such as the decrease in temperature with elevation. By adding the long-term monthly mean temperature for the Earth to each anomaly value, one can create a time series that approximates the temperature of the Earth and how it has been changing through time.

An Operational Near Real Time Global Temperature Index

An Operational Near Real Time Global Temperature Index

Robert G. Quayle, Thomas C. Peterson, Alan N. Basist, and Catherine S. Godfrey
National Climatic Data Center (NCDC), NOAA/NESDIS, Asheville N.C.

Abstract. To capture the global land surface temperature signal in a timely way, a blend of traditional long-term in situ climatic data sets, combined with real time Global Telecommunications System monthly CLIMAT summaries is employed. For the global sea surface, long-term ship data climatologies are combined with a blend of ship, buoy, and satellite data to provide the greatest possible coverage over the oceans. The result is a global century-scale surface temperature index that closely parallels other widely published global surface temperature measurements and can be updated monthly a week or two after the end of a data month.

Introduction

The Third Session of the Conference of the Parties to the U.N. Framework Convention on Climate Change in Kyoto, Japan, was but one client of NOAA that needed quick and authoritative information on century scale climate perspectives in a near-real-time mode. NCDC was able to offer help, and this work documents the methodology which has been used since that time. It seems paradoxical that we need near-real-time data for a system that responds as slowly as climate, but recent paleoclimatic evidence and the recent warmth of the globe suggest that this paradigm is not always justified. Moreover, as nations struggle to develop effective environmental policies, the observed data become a critical part of these ongoing discussions; and the meteorological infrastructure of the globe is also geared to real-time operation. Therefore, both the need for, and the capability for delivering near-real-time climatic analyses are quite real. In fact, timely climatic information (provided when there is a maximum of interest) may be the best way to provide the most reliable information to the greatest number of people.

Surface Land Temperatures

Surface land air temperature (LAT) climatology (at instrument shelter height) is derived from the Global Historical Climatology Network version 2 data set (GHCN, Peterson and Vose 1997). GHCN v.2 includes previously unavailable Colonial Era data that fill in data sparse times and places (Peterson and Griffiths 1997). All data are processed via the Climate Analysis System (CAS) developed at NCDC. The update system subjects the most recent data to a rigorous quality control (Peterson et al. 1998a). Its unique duplicate preservation scheme preserves the integrity of the input data streams (Peterson and Vose 1997). The First-Difference area averaging technique thrives on these duplicates and maximizes the global data available for analysis (Peterson et al. 1998b). Homogeneity adjustment procedures developed over several years assures objective, reproducibly homogeneous time series (Peterson and Easterling 1994, Easterling and Peterson 1995, Peterson et al. 1998c). Data volume varies from several hundred stations per year to several thousand (Peterson and Vose, 1997). For 1997, over 14,000 individual station monthly records are used in the analysis to produce 5x5 degree grid box data that are summarized into hemispheric and global averages.

Sea Surface Temperatures

The Global Ocean Surface Temperature Atlas (GOSTA, Bottomley et al. 1990), provides a century+ global record of 5x5 degree grid box in situ Sea Surface Temperature (SST) means by year through 1996. For this application, we use the U. K. Meteorological Office version, called UKMO HSST in the form of anomalies with respect to a 1961-90 averaging period (Folland et al. 1993). For near real time updates, the most timely and geographically complete data available are the National Centers for Environmental Prediction - Optimum Interpolation (NCEP OI) blended satellite, ship and buoy SST data set (Reynolds and Smith 1994), also in monthly 5x5 degree grid box format, available for all years since 1982. NCDC produced global averages and the accompanying anomaly series from both data sets. To produce a long time series (beginning in 1880) with maximum contemporary coverage, these two SST data sets are combined.

larger image National Climatic Data Center's global record of 5x5 degree box in situ SST means by year through 1996.

larger image National Centers for Environmental Prediction-Optimum Interpolation blended satellite, ship, and buoy SST data set.

To fuse the two time series, a simple linear regression is performed for global monthly (and annual) mean anomalies for the years 1982-1997, using NCEP OI SST with respect to 1982-1997 as the dependent variable and UKMO HSST with respect to 1961-90 as the independent variable. A plot of the annual means is shown in Fig. 1). The fit is very good (r = 0.93) considering the areas covered are somewhat different, with ship data available primarily along shipping lanes, and blended NCEP OI data being virtually global. The relationship between global mean annual modeled NCEP OI SST anomalies (SST_OI) and UKMO HSST anomalies (SST_UK) is described via the regression equation:

(1) SST_OI = 0.80 SST_UK - 0.15, where anomalies are in deg. C.

The offset, -.15, adjusts the averaging period for the modeled NCEP OI SST anomaly to 1961-90, while the .8 factor reflects the reduced trend of NCEP OI SST compared to the UKMO data. A similar relationship exists for each month. Using the monthly equations, UKMO HSST data are converted to modeled NCEP OI SST anomalies (from 1961-90 means) for each month from 1880 thru 1981. The NCEP OI SST data are appended to this record, and are updated shortly after the end of each data month. For plotting purposes, the data are then adjusted to anomalies from a 1880-1997 averaging period. Fig. 2 is a plot of these data from 1950 to 1997 (upper) and 1880 to 1997 (lower). On a globally averaged basis, the NCEP OI data are somewhat cooler than the UKMO HSST data, but the reasons are not yet fully known. Possibly, (1) the use of modeled SST data in the vicinity of the ice edge by UKMO HSST creates a warmer strip of water in polar areas; and (2) the use of satellite AVHRR Multi-Channel SSTs, uncorrected by ship and buoy data in some extremely data-sparse, areas creates a modest cooling (because of skin temperature effects).

 

larger image A simple linear regression was performed for global annual mean anomalies for the years 1982-1996, using NCEP-OI SST with respect to 1982-1996 as the independent variable and Global Ocean Surface Temperature Atlas (GOSTA) SST with respect to 1961-90 as the dependent.

larger image Global mean annual Reynolds SST anomalies (SSTR) were converted tomodeled GOSTA-compatible SST anomalies (SSTM) for each year, 1982-1997, and the new values were spliced onto the GOSTA record for the years after 1982.

The Global Index

 

NCDC now has readily updatable global Surface Land Air Temperature (LAT) and global SST anomalies through the latest month of complete SST and CLIMAT (World Meteorological Organization encoded data transmitted over the Global Telecommunications System 2 to 10 days after the end of a data month. Note that the LAT data set is essentially independent from the SSTs, and LATs are summarized independently from SSTs. To combine these data into a simple index, the LAT is weighted with a coefficient of 0.3 (since about 30% of the surface of the Earth is land) and the SST with 0.7 (as the globe is about 70% ocean). The result is shown in Fig. 3. It is called an index (as it is a combination of air and sea temperatures, and ignores ice-covered sea). When the new index is compared to similar data developed at the NASA Goddard Institute for Space Studies (www.giss.nasa.gov, documented in Hansen and Lebedeff 1987; Reynolds and Smith 1994; Smith et al. 1996), the match is very good (r=0.95) for the period for which Hansen has a land-ocean product (1950 to the present, also using NCEP OI SST). The match (r= 0.87) with the current global benchmark surface data set (Jones 1994 with updates, Fig. 4) for the period 1880-1996 is also relatively good, particularly for a near-real time index.

 

In summary, we believe we have combined the three best data sets in the world for their respective specialties: UKMO HSST for long-term SST; NCEP OI SST for recent decades; and the GHCN for global land surface temperatures. While not sophisticated, the technique is robust and the results, predictably, compare favorably with other widely used analyses.

 

larger image The combined land-air surface temperature and sea surface temperatures global climatological index; the data indicates that 1997 was probably the warmest year of the century.

larger image The trends demonstrated by this United Kingdom product for global temperatures were in agreement with the NCDC's global climatological index.

References

 

Bottomley, M., C.K. Folland, J. Hsiung, R. E. Newell, D. E. Parker, Global Ocean Surface Temperature Atlas "GOSTA", Bracknell [England] U.K.: Meteorological Office;

[Cambridge]: Massachusetts Institute of Technology, 1990. (337pp) Also see: http://www.meto.govt.uk/sec5/CR_div/climate_index/hadley_mohsst.html

 

Easterling, David R. and Thomas C. Peterson, A new method for detecting and adjusting for undocumented discontinuities in climatological time series. Int. J. Climatol. 15 (4), 369-377, 1995.

 

Folland, C. K., R. W. Reynolds, M. Gordon, and D. E. Parker, A study of six operational sea surface temperature analyses. J. Climate 6 (1), 96-113, 1993.

 

Hansen, J., and S. Lebedeff, Global trends of measured surface air temperature. J. Geophys. Res. 92 (D11) , 13,345-13,372, 1987. Updated at http://www.giss.nasa.gov/.

 

Hansen, J., R. Ruedy, M. Sato, and R. Reynolds, Global surface air temperature in 1995: Return to pre-Pinatubo level. Geophys. Res. Lett 23 (13), 1665-1668, 1996.

 

Jones, P.D., Hemispheric surface air temperature variations: A reanalysis and update to 1993. J. Climate 7 (11), 1794-1802, 1994.

 

Peterson, Thomas C. and David R. Easterling, Creation of homogeneous composite climatological reference series. Int. J. Climatol. 14 (6), 671-679, 1994.

 

Peterson, Thomas C. and Russell S. Vose, An overview of the Global Historical Climatology Network temperature data base. Bull. Amer. Meteor. Soc. 78 (12), 2837-2849, 1997.

 

Peterson, Thomas C. and John F. Griffiths, Historical African data. Bull. Amer. Meteor. Soc. 78 (12), 2869-2871, 1997.

 

Peterson, Thomas C., Russell S. Vose, Richard Schmoyer, and Vyachevslav Razuva_v, Quality control of monthly temperature data: The GHCN experience. Int. J. Climatol., (in press), 1998a.

 

Peterson, Thomas C., Thomas R. Karl, Paul F. Jamason, Richard Knight, and David R. Easterling, The First Difference Method: Maximizing Station Density for the Calculation of Long-term Global Temperature Change. J. Geophys. Res. (Atm.), (in press), 1998b.

 

Peterson, T. C., D. R. Easterling, T. R. Karl, P. Ya. Groisman, N. Nicholls, N. Plummer, S. Torok, I. Auer, R. Boehm, D. Gullett, L. Vincent, R. Heino, H. Tuomenvirta, O. Mestre, T. Szentimre, J. Salinger, E. Førland, I. Hanssen-Bauer, H. Alexandersson, P. Jones, D. Parker, Homogeneity adjustments of in situ atmospheric climate data: A review. Int. J. Climatol., (in press), 1998c.

 

Reynolds, R. W. and T. M. Smith, Improved global sea surface temperature analyses using optimum interpolation. J. Climate 7 (6), 929-948, 1994.

 

Smith, T.M., R.W. Reynolds, R.E. Livezey, and D.C. Stokes, Reconstruction of historical sea surface temperature using empirical orthogonal functions. J. Climate 9 (6), 1403-1420, 1996.

 

Global Long-term Mean Land and Sea Surface Temperatures

Estimates of mean monthly global surface temperatures are given below with respect to the long-term period 1880 to 2000. The figures are based on 1961-1990 estimates from the University of East Anglia’s Climate Research Unit (UEA-CRU). The recently derived 1961-1990 global monthly surface temperature averages represent, in our opinion, the best absolute estimates of global mean temperature and were compiled at UEA-CRU by M. New, P.D. Jones, D.E. Parker and others . The data and methods used are described here and in current and forthcoming publications (see below).

The UEA-CRU 1961-1990 estimates have been separated into land and sea components and adjusted using the longer-term global temperature anomaly time series from NCDC. The figures presented below therefore are mean monthly global surface temperature estimates for the entire period of reliable temperature records, 1880 to 2000. Estimates for land (including Antarctica) and sea surface areas for the period 1880 to 2000 are given separately and in combined form.

Absolute estimates of global mean surface temperature are difficult to compile for a number of reasons. Since some regions of the world have few temperature measurement stations (e.g., the Sahara Desert), interpolation must be made over large, data sparse regions. In mountainous areas, most observations come from valleys where the people live so consideration must be given to the effects of elevation on a region’s average as well as to other factors that influence surface temperature. Consequently, the estimates below, while considered the best available, are still approximations and reflect the assumptions inherent in interpolation and data processing. Time series of monthly temperature records are more often expressed as departures from a base period (e.g., 1961-1990, 1880-2000) since these records are more easily interpreted and avoid some of the problems associated with estimating absolute surface temperatures over large regions. For a brief discussion of using temperature anomaly time series see the Climate of 1998 series.

The global monthly surface temperature averages in the table below can be added to a given month’s anomaly (departure from the 1880 to 2000 base period average) to obtain an absolute estimate of surface temperature for that month. (Files of absolute estimates are provided below.)

Erratum: Please note that prior to 26 June 2000, the mean values added to the land and ocean anomalies were incorrect. These data are now correct. Analysis of trends in the time series would not be impacted by this error since the error involved adding a constant to the entire period of record.

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

Jones, P. D., M. New, D. E. Parker and S. Martin, submitted: Surface air temperature and its changes over the past 150 years. Rev. Geophys.

This climatology is actually a combination of four separate data sets:

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.

The Global Anomalies and Index

As data are being received, scientists at The National Oceanic and Atmospheric Administration's National Climatic Data Center are automatically updating The Global Historical Climatology Network data base to maintain a global climate perspective in near real-time.
• The Monthly Global Land Temperature Anomalies (degrees C)
  
• The Monthly Global Land Temperature Anomalies (degrees F)
  
• The Monthly Global Ocean Temperature Anomalies (degrees C)
  
• The Monthly Global Ocean Temperature Anomalies (degrees F)
  
• The Monthly Global (land and ocean combined into an anomaly) Index (degrees C)
  
• The Monthly Global (land and ocean combined into an anomaly) Index (degrees F)

• The Annual Global Land Temperature Anomalies (degrees C)
  
• The Annual Global Ocean Temperature Anomalies (degrees C)
  
• The Annual Global (land and ocean combined) Anomalies (degrees C)
  ...through the most recent complete year (2000).

• The Monthly Absolute Estimates of Global Land Temperature (degrees C)
  
• The Monthly Absolute Estimates of Global Land Temperature (degrees F)
  
• The Monthly Absolute Estimates of Global Ocean Temperature (degrees C)
  
• The Monthly Absolute Estimates of Global Ocean Temperature (degrees F)
  
• The Monthly Absolute Estimates of Global (land and ocean combined) Temperature Index (degrees C)
  
• The Monthly Absolute Estimates of Global (land and ocean combined) Temperature Index (degrees F)
  ...all with respect to the long-term (1880-2000) mean.

NCDC / Climate Resources / Climate of 1998 / 1999 / Anomalies / Search / Help