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Vertically resolved ozone

Combined vertical ozone profile database - version 1.0
An updated, improved version of the Bodeker Scientific monthly mean vertical ozone profile database, spanning the period 1979 to 2016, is now available for download. The database combines measurements from several satellite-based instruments and ozone profile measurements from the global ozonesonde network. Monthly mean zonal mean ozone concentrations in mixing ratio and number density are provided in 5o latitude zones, spanning 70 altitude levels (1 to 70 km), or 70 pressure levels that are approximately 1 km apart (878.4 hPa to 0.046 hPa). Different data sets or 'Tiers' are provided and all data sets can be obtained from our FTP server at
. Login details for the FTP server can be obtained by emailing

If you would like to be informed of updates to this database, please email Greg Bodeker at and he will add you to his list. The naming convention for all NetCDF files is where:
  • XX is either 'MR' for mixing ratio or 'ND' for number density.
  • YYY is either 'PRS' to denote that the data are on pressure levels or 'GPH' to denote that the data are on geopotential height levels.
  • ZZ denotes the Tier: '0.0', '0.5', '1.1', '1.2', '1.3' or '1.4'
Tier 0
The Tier 0 database comprises monthly mean zonal mean ozone concentrations based only on the
available measurements and therefore does not completely cover the whole globe or the full vertical range uniformly. Measurements from satellites and ozonesondes are corrected for drifts and biases using output from a chemical transport model as a transfer standard. The corrected measurements are then accumulated into monthly mean zonal mean values.

Tier 0.5
The Tier 0.5 database is a filled database where at 20 km a regression of available monthly mean zonal mean ozone concentrations against monthly mean zonal mean total column ozone, calculated from our total column ozone database, is used to create a gap-free ozone field at 20 km. The filled ozone field at 20 km is then used as a predictor for ozone concentrations at 21 km. For levels 22 km and above, we use the data at levels N-1 and N-2 as linear predictors for the values at level N. Similarly, below level 20, we use values at level N+1 and N+2 as linear predictors for values at level N. The result is a filled database that largely captures real-word variability.

Tier 1.1 to 1.4
For the Tier 1.x data, a least squares regression model is used to attribute variability to various known forcing factors for ozone. These four databases result from applying a regression model to the Tier 0 data sets, supplemented by Tier 0.5 data where Tier 0 data are missing. The regression model facilitates attribution to known ozone forcings, e.g. EESC, solar cycle, QBO etc.. The regression model is of the form:

Ozone(t,lat) =  A(t,lat) +                              Offset and seasonal cycle
              B(t,lat) x t +                         Linear trend
              C(t,lat) x EESC(t,AoA) +      Age-of-air dependent equivalent effective stratospheric chlorine
              D(t,lat) x QBO(t) +              Quasi-biennial Oscillation 
              E(t,lat) x QBOorthog(t) +     Orthogonalized QBO
              F(t,lat) x ENSO(t) +             El-Niño Southern Oscillation 
              G(t,lat) x Solar(t) +             Solar cycle
              H(t,lat) x Pinatubo(t) +      Mt. Pinatubo volcanic eruption
              R(t)                                       Residual

Regression model fit coefficients are expanded in Fourier series to account for seasonality and in Legendre polynomials in latitude to account for meridional structure in the fit coefficients. Regression model output is then used to produce 4 gap free Tier 1.x data sets by combining the contributions from different forcings into 4 different databases that include different contributions:
  • Tier 1.1 (Anthropogenic): This comprises the mean annual cycle plus contributions from the EESC and linear trend basis functions.
  • Tier 1.2 (Natural): This comprises the mean annual cycle plus contributions from the QBO, solar cycle and El Niño basis functions.
  • Tier 1.3 (Natural & volcanoes): Tier 1.2 but now also including contributions from volcano basis functions.
  • Tier 1.4 (All): Constructed by summing the contributions from all basis functions.
Since this is output from a regression model, it does not capture real-world year-to-year variability - only the variability for which we have basis functions in the regression model.
This data set is completely filled such that there are no missing data. A publication describing this database is currently in preparation.

The work for this project has been supported by the New Zealand Deep South National Science Challenge - contract CO1X1445. This database is made freely available for research purposes. If you are going to be using this database in a publication, please let us know. At the very least please include the following acknowledgement: We would like to thank Greg Bodeker , Stefanie Kremser (Bodeker Scientific) and Birgit Hassler (DLR) for providing the combined vertical ozone profile database.