Spectral approach to optimal estimation of the global average temperature
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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
author list (cited authors)
Shen, S., North, G. R., & Kim, K. Y.
complete list of authors
Shen, SSP||North, GR||Kim, KY