Terrestrial Air Temperature:
1900-2010 Gridded Monthly Time Series

(Version 3.01)

interpolated and documented by

Kenji Matsuura and Cort J. Willmott
[with support from NASA's Innovation in Climate Education (NICE) Program]

For additional information concerning this archive,
please contact us at:

Department of Geography
University
of Delaware
Newark, DE 19716
(302) 831-2294

or

kenjisan@udel.edu


Archive (Version 3.01) released in June, 2012


STATION DATA SOURCES:

Station data, monthly-mean air temperature (T, oC), were compiled from several updated sources including a recent version of the Global Historical Climatology Network (GHCN2), Peterson and Vose, 1997); the Atmospheric Environment Service/Environment Canada; the State Hydrometeorological Institute, St. Petersburg, Russia; Greenland—from the GC-Net (Steffen et al., 1996); the Automatic Weather Station Project (courtesy of Charles R. Stearns at the University of Wisconsin-Madison); the Global Synoptic Climatology Network (Dataset 9290c, courtesy of National Climatic Data Center); and the Global Surface Summary of Day (GSOD) (NCDC).

For stations and months with GHCN2 observations, the GHCN2 observations were used as “our” T values, because of GHCN2 quality-control measures.  When and where GHCN2 observations were unavailable, other station records often were used and, in some cases, merged to create composite monthly station-record series.  During this process, station records that had the same geographical coordinates were interleaved or blended to create a single, station time series for that location. For some data sets, monthly values were compiled from hourly or daily values first. If there were two or more monthly-average station observations for a given month, the median of these values was taken as T for that month. When there was only one monthly station observation for a month, it was taken as T for that month. This was done to make use of all available data. Observations from stations which had different geographical coordinates were assumed to belong to different station records, although sometimes parts of nearby station records were extremely similar. For some data sets, monthly values were derived from daily values; that is, when the number of missing days was no more than five days during a month.

Air temperature observations from the GSOD archive also were used, but some seemed unrealistic. Therefore, we developed and applied a filter to the monthly GSOD values. A monthly T was estimated at (interpolated to) a GSOD station location from a station network only based on GHCN2 stations. A GHCN2-estimated time series of such values then was compared to the GSOD observations at a GSOD station, and a cumulative sum of the differences between the GHCN2 estimates and the GSOD observations was evaluated. A GSOD station which exhibited a large cumlative sum was suspected of having erroneous observations, and the GHCN2 estimates and GSOD values were visually inspected. Those GSOD observations that showed clear discrepancies with the GHCN2 estimates were removed from consideration. The resultant number of station records available for the 1900 - 2010 period ranges from about 1,600 to about 12,300.

SPATIAL INTERPOLATION:

Monthly averages of station air temperature (T) were interpolated to a 0.5 degree by 0.5 degree latitude/longitude grid, where the grid nodes are centered on the 0.25 degree. The gridded fields were estimated from monthly weather-station averages using a combination of spatial interpolation methods: digital-elevation-model (DEM) assisted interpolation (Willmott and Matsuura, 1995); traditional interpolation (Willmott et al., 1985); and climatologically aided interpolation (CAI) (Willmott and Robeson, 1995). A climatology of T for each month at stations was produced first by combining average monthly station values from two available climatologies (described below).  DEM-assisted interpolation then was used to estimate average monthly Ts (climatology) at unsampled locations, principally at the grid nodes.  Individual monthly gridded T fields were estimated next using CAI (described below).

For the background climatology, two station climatologies were merged. The first was calculated at those of our air-temperature time-series stations which had at least ten years of observations for each month. The second was the monthly station T climatology of Legates and Willmott (1990). Only those Legates and Willmott stations which were not collocated with our own station climatology were included in the background climatology for CAI.

Traditional interpolation was accomplished with a spherical version of Shepard’s algorithm, which employs an enhanced distance-weighting method (Shepard, 1968; Willmott et al., 1985).  Our traditional interpolations of estimated sea-level Ts, within our DEM-assisted procedure, as well as our interpolations of deviations from climatology, within CAI, were made in this way.  The number of nearby stations that influence a grid-node estimate, however, was increased to an average of 20, from an average of 7 in earlier applications. This resulted in smaller cross-validation errors (see below) and visually more realistic air-temperature fields. A more robust neighbor finding algorithm, based on spherical distance, also was used.

Incorporating elevational influences, through an average air-temperature lapse rate, can increase the accuracy of spatially interpolating average air temperature (Willmott and Matsuura, 1995). DEM-assisted interpolation of average-monthly air temperature, therefore, was employed. Briefly, each average-monthly station air temperature was first “brought down” to sea level (warmed) at an average environmental lapse rate (6.0 deg C/km). Traditional interpolation then was performed on the adjusted-to-sea-level average-monthly station air temperatures. Finally, the gridded sea-level air temperatures were brought up to the DEM-grid height (cooled); once again, at the average environmental lapse rate.

Using a relatively high-resolution climatology also can increase the accuracy of spatially interpolated time series of monthly climate variables. Employing CAI (Willmott and Robeson, 1995), a monthly T at each time-series station was differenced from a climatologically averaged T for that month which was available at or was interpolated to the time-series station location. Traditional interpolation then was performed on the station differences to obtain a gridded difference field. Finally, the gridded difference field was added to the interpolated (DEM-assisted) estimates of the climatology at the same set of grid points.

SPATIAL CROSS VALIDATION:

To indicate (roughly) the spatial interpolation errors, station-by-station cross validation was employed (Willmott and Matsuura, 1995). One station was removed at a time, and the air temperature was then interpolated to the removed station location from the surrounding nearby stations. The difference between the real station value and the interpolated value is a local estimate of interpolation error. After each station cross validation was made, the removed station was put back into the network. To reduce network biases on cross-validation results, absolute values of the errors at the stations were interpolated to the same spatial resolution as the air temperature field.

ARCHIVE STRUCTURE:

Global2011T.tar.gz:

Monthly-mean air temperatures for the years 1900-2010 interpolated to a 0.5 by 0.5 degree grid resolution (centered on 0.25 degree). The format of each record is:

 

Field

Columns

Variable

Fortran Format

1

1 - 8

Longitude (decimal degrees)

F8.3

2

9 - 16

Latitude (decimal degrees)

F8.3

3-14

17 - 112

Monthly Air Temperature (oC, Jan-Dec)

12F8.1

 

Global2011TCv.tar.gz:

Cross-validation errors (absolute values) associated with air temperatures for the years 1900-2010 interpolated to a 0.5 by 0.5 degree grid resolution. The format of each record is:

 

Field

Columns

Variable

Fortran Format

1

1 - 8

Longitude (decimal degrees)

F8.3

2

9 - 16

Latitude (decimal degrees)

F8.3

3-14

17 - 112

Cross-validation errors (absolute values) of Monthly Temperature (oC, Jan-Dec)

12F8.1

 

SELECTED REFERENCES:

 

Legates, D.R. and C.J. Willmott (1990).  Mean seasonal and spatial variability in global surface air temperature. Theoretical and Applied Climatology, 41, 11-21.

Peterson, T. C., R. S. Vose R. Schmoyer and V. Razuvaëv (1998). Global Historicl Climatology Network (GHCN) Quality Control of Monthly Temperature Data. International Journal of Climatology, 18, 1169-1179.

Peterson, T.C. and R.S. Vose (1997). An overview of the Global Historical Climatology Network temperature database. Bulletin of the American Meteorological Society, 78, 2837-2849.

Shepard, D. (1968). A two-dimensional interpolation function for irregularly-spaced data. Proceedings, 1968 ACM National Conference, 517-523.

Steffen, K., J.E. Box, and W. Abdalati (1996). Greenland Climate Network: GC-Net. Colbeck, S. C. Ed. CRREL 96-27 Special Report on Glaciers, Ice Sheets and Volcanoes, trib. to M. Meier, 98-103.

 

Willmott, C.J. and K. Matsuura (1995). Smart interpolation of annually averaged air temperature in the United States. Journal of Applied Meteorology, 34, 2577-2586.

 

Willmott, C.J. and S.M. Robeson (1995).  Climatologically aided interpolation (CAI) of terrestrial air temperature. International Journal of Climatology, 15(2), 221-229.

                                                
Willmott, C.J., C.M. Rowe and W.D. Philpot (1985). Small-scale climate maps: a sensitivity analysis of some common assumptions associated with grid-point interpolation and contouring. American Cartographer, 12, 5-16.