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Volume 5, issue 1
Earth Syst. Sci. Data, 5, 31–43, 2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: Changes in the vertical distribution of ozone – the SI2N report...

Earth Syst. Sci. Data, 5, 31–43, 2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

  11 Feb 2013

11 Feb 2013

A vertically resolved, global, gap-free ozone database for assessing or constraining global climate model simulations

G. E. Bodeker1,2, B. Hassler3,4, P. J. Young3,4, and R. W. Portmann4 G. E. Bodeker et al.
  • 1Bodeker Scientific, 42 Young Lane RD1, Alexandra, 9391, New Zealand
  • 2New Zealand Climate Change Research Institute, Victoria University, New Zealand
  • 3Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO, 80309-0216, USA
  • 4Chemical Sciences Division, NOAA Earth System Research Laboratory, R/CSD08, 325 Broadway Boulder, CO, 80305-3328, USA

Abstract. High vertical resolution ozone measurements from eight different satellite-based instruments have been merged with data from the global ozonesonde network to calculate monthly mean ozone values in 5° latitude zones. These ''Tier 0'' ozone number densities and ozone mixing ratios are provided on 70 altitude levels (1 to 70 km) and on 70 pressure levels spaced ~ 1 km apart (878.4 hPa to 0.046 hPa). The Tier 0 data are sparse and do not cover the entire globe or altitude range. To provide a gap-free database, a least squares regression model is fitted to the Tier 0 data and then evaluated globally. The regression model fit coefficients are expanded in Legendre polynomials to account for latitudinal structure, and in Fourier series to account for seasonality. Regression model fit coefficient patterns, which are two dimensional fields indexed by latitude and month of the year, from the N-th vertical level serve as an initial guess for the fit at the N + 1-th vertical level. The initial guess field for the first fit level (20 km/58.2 hPa) was derived by applying the regression model to total column ozone fields. Perturbations away from the initial guess are captured through the Legendre and Fourier expansions. By applying a single fit at each level, and using the approach of allowing the regression fits to change only slightly from one level to the next, the regression is less sensitive to measurement anomalies at individual stations or to individual satellite-based instruments. Particular attention is paid to ensuring that the low ozone abundances in the polar regions are captured. By summing different combinations of contributions from different regression model basis functions, four different ''Tier 1'' databases have been compiled for different intended uses. This database is suitable for assessing ozone fields from chemistry-climate model simulations or for providing the ozone boundary conditions for global climate model simulations that do not treat stratospheric chemistry interactively.