ACP Atmospheric Chemistry and Physics ACP 1680-7324 Copernicus GmbH Göttingen, Germany 10.5194/acp-6-525-2006 Statistical diagnostic and correction of a chemistry-transport model for the prediction of total column ozone Guillas S. 1 Tiao G. C. 2 Wuebbles D. J. 3 Zubrow A. 4 School of Mathematics, Georgia Institute of Technology, Atlanta, Georgia, USA Graduate School of Business, University of Chicago, Chicago, Illinois, USA Department of Atmospheric Sciences, University of Illinois, Urbana, Illinois, USA Center for Integrating Statistical and Environmental Science, University of Chicago, Chicago, Illinois, USA 21 02 2006 6 2 525 537 This is an open-access article ditributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. This article is available from http://www.atmos-chem-phys.net/6/525/2006/acp-6-525-2006.html The full text article is available as a PDF file from http://www.atmos-chem-phys.net/6/525/2006/acp-6-525-2006.pdf

In this paper, we introduce a statistical method for examining and adjusting chemical-transport models. We illustrate the findings with total column ozone predictions, 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 in total ozone over the latitudes ranging from 60&deg; South to 60&deg; North to see if the model captures some typical patterns in the data. The method proceeds in two steps to avoid possible collinearity issues. First, we regress the measurements given by a cohesive data set from the SBUV(/2) satellite system on the model outputs with an autoregressive noise component. Second, we regress the residuals of this first regression on the solar flux, the annual cycle, the Antarctic or Arctic Oscillation, and the Quasi Biennial Oscillation. If the coefficients from this second regression are statistically significant, then they mean that the model did not simulate properly the pattern associated with these factors. Systematic anomalies of the model are identified using data from 1979 to 1995, and statistically corrected afterwards. The 1996&ndash;2003 validation sample confirms that the combined approach yields better predictions than the direct UIUC 2-D outputs.