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Climate projections: Past performance no guarantee of future skill?
Author(s) -
Reifen C.,
Toumi R.
Publication year - 2009
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/2009gl038082
Subject(s) - ensemble average , ensemble forecasting , exponent , statistics , systematic error , climate change , environmental science , climate model , climatology , econometrics , mathematics , statistical physics , meteorology , geology , physics , oceanography , linguistics , philosophy
The principle of selecting climate models based on their agreement with observations has been tested for surface temperature using 17 of the IPCC AR4 models. Those models simulating global mean, Siberian and European 20th Century surface temperature with a lower error than the total ensemble for one period on average do not do so for a subsequent period. Error in the ensemble mean decreases systematically with ensemble size, N , and for a random selection as approximately 1/ N α , where α lies between 0.6 and 1. This is larger than the exponent of a random sample ( α = 0.5) and appears to be an indicator of systematic bias in the model simulations. There is no evidence that any subset of models delivers significant improvement in prediction accuracy compared to the total ensemble.