A 2-D Model Diagnostic and Correction for the Prediction of Total Column Ozone

• Activity Number: 19
• Type: Topic Contributed
• Date/Time: Sunday, August 7, 2005 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics and the Environment
• Author(s): Serge Guillas*+ and George Tiao and Donald J. Wuebbles and Alexis Zubrow
• Companies: Georgia Institute of Technology and The University of Chicago and University of Illinois, Urbana Champaign and The University of Chicago
• Keywords: stratospheric ozone ; model diagnostics ; prediction

Abstract

We introduce a hybrid statistical chemical-transport model for total column ozone prediction based on the University of Illinois' 2-D chemical-transport model of the global atmosphere. We propose a general diagnostic procedure for the model outputs in total ozone over the latitudes ranging from 60 degrees south to 60 degrees north to see if the model captures typical patterns in the data. The method proceeds in two steps. First, we regress the measurements given by a cohesive dataset 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 Quasibiennial Oscillation. If the coefficients from this second regression are statistically significant, 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 afterward. The 1996--2003 validation sample confirms the combined approach yields better predictions than the direct UIUC 2-D outputs.