Arctic Land-Surface Air Temperature:
1930-2004 Gridded Monthly Time Series

(Version 1.03)

interpolated and documented by

Kenji Matsuura and Cort J. Willmott
(with support from the Arctic RIMS Project
at the University of New Hampshire)

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

Center for Climatic Research
Department of Geography
University of Delaware
Newark, DE 19716
(302) 831-2294

or

kenjisan@udel.edu


Archive (Version 1.03) created December, 2005

STATION DATA SOURCES:

Station data, monthly-mean air temperature (T, deg. C), were compiled from several updated sources including a recent version of the Global Historical Climatology Network (Peterson and Vose, 1998); the Atmospheric Environment Service/Environment Canada; the State Hydrometeorological Institute, St. Petersburg, Russia; Greenland from the GC-Net (Steffen, 1996); the Automatic Weather Station Project (courtesy of Charles R. Stearns at the University of Wisconsin-Madison); Global Synoptic Climatology Network (Dataset 9290c, courtesy of National Climatic Data Center); and Global Surface Summary of Day (NCDC). The station records drawn from these data sets were merged to create a composite station-record series for the period 1930 through 2004. 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 station observations for a given month, these observations were averaged to obtain T for that month. When there was only one 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. The resultant number of stations located north of 45 deg. N is about 12,300.

SPATIAL INTERPOLATION:

Traditional interpolation was accomplished with the spherical version of Shepard's algorithm, which employs an enhanced distance-weighting method (Shepard, 1968; Willmott et al., 1985). Station averages of air temperature were interpolated to a 0.5 degree by 0.5 degree of latitude/longitude grid, where the grid nodes are centered on 0.25 degree. The number of nearby stations that influence a grid-node estimate 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 further increase the accuracy of spatially interpolating average air temperature (Willmott and Matsuura, 1995). Digital-elevation-model- or DEM-assisted interpolation of air temperature, therefore, was employed. Briefly, station air temperature was first "brought down" to sea level at an average environmental lapse rate (6.0 deg C/km). Traditional interpolation then was performed on the adjusted-to-sea-level station air temperatures. Finally, the gridded sea-level air temperatures were brought up to the DEM-grid height, again, at the average environmental lapse rate.

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

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 five years of observations for each month (within the period 1960-1990). 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 1960-1990 station climatology were included in the background climatology for CAI.

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:

SELECTED REFERENCES:

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., 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.

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.