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In this paper, we introduce a statistical method for examining and adjustingchemical-transport models. We illustrate the findings with total column ozonepredictions, based on the University of Illinois at Urbana-Champaign 2-D(UIUC 2-D) chemical-transport model of the global atmosphere.<P  style="line-height: 20px;">We propose a general diagnostic procedure for the model outputs intotal ozone over the latitudes ranging from 60°South to 60° North to seeif the model captures some typical patterns in the data.The method proceeds in two steps to avoid possiblecollinearity issues. First, we regress the measurements given by acohesive data set from the SBUV(/2) satellite system on the modeloutputs with an autoregressive noise component.Second, we regress the residuals of this first regression onthe solar flux, the annual cycle, the Antarctic or Arctic Oscillation,and the Quasi Biennial Oscillation. If thecoefficients from this second regression are statisticallysignificant, then they mean that the model did not simulate properlythe pattern associated with these factors.Systematic anomalies of the model are identified using data from 1979to 1995, and statistically corrected afterwards. The 1996–2003 validation sampleconfirms that the combined approach yields better predictions than thedirect UIUC 2-D outputs

Statistical diagnostic and correction of a chemistry-transport model for the prediction of total column ozone