Spectral approach to optimal estimation of the global average temperature

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

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