Spectral approach to optimal estimation of the global average temperature Academic Article uri icon


  • Making use of EOF analysis and statistical optimal averaging techniques, the problem of random sampling error in estimating the global average temperature by a network of surface stations has been investigated. The EOF representation makes is unnecessary to use simplified empirical models of the correlation structure of temperature anomalies. Comparisons with the 100-yr UK dataset show that correlations for the time series of the global temperature anomaly average between the full dataset and this study's sparse configurations are rather high. For example, the 63-station Angell-Korshover network with uniform weighting explains 92.7% of the total variance, whereas the same network with optimal weighting can lead to 97.8% explained total variance of the UK dataset. -from Authors

published proceedings

  • Journal of Climate

altmetric score

  • 8.7

author list (cited authors)

  • Shen, S., North, G. R., & Kim, K. Y.

citation count

  • 49

complete list of authors

  • Shen, SSP||North, GR||Kim, KY

publication date

  • January 1994