January 10, 2000
Robert G. Quayle, Thomas C. Peterson, Alan N. Basist, and
Catherine S. Godfrey
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.
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.
(1) SSTOI = 0.80 SSTUK - 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).
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.
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 TemperaturesMatt Menne
March 15, 2000
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.
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.)
Global Mean Monthly Surface Temperature Estimates for the Base Period 1880 to 2000
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:
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.
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.
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.
Last Updated 15 February 2001 by Jay Lawrimore mailto:%email@example.com and Tom Ross firstname.lastname@example.org.