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

Bodeker, G. E.; Haßler, Birgit; Young, Paul; Portmann, R. W.

doi:10.5194/essd-5-31-2013

Cited by 58 publications

(102 citation statements)

References 35 publications

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“…This database was used by CMIP5 climate models that did not simulate their own stratospheric ozone, and it also covered the troposphere since tropospheric ozone also has a radiative effect on the climate system. A first database that covered the troposphere and the stratosphere, based solely on measurements and using statistical methods for data gap filling, was introduced by Bodeker et al (2013) (Binary Database of Profiles,hereafter BDBP v1.1.0.6) and is the predecessor of the database described here.…”

Section: Introductionmentioning

confidence: 99%

An updated version of a gap-free monthly mean zonal mean ozone database

Haßler

Kremser

Bodeker

et al. 2018

Earth Syst. Sci. Data

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Abstract. An updated and improved version of a global, vertically resolved, monthly mean zonal mean ozone database has been calculated -hereafter referred to as the BSVertOzone (Bodeker Scientific Vertical Ozone) database. Like its predecessor, it 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 5 • latitude bins, spanning 70 altitude levels (1 to 70 km), or 70 pressure levels that are approximately 1 km apart (878.4 to 0.046 hPa). Different data sets or "tiers" are provided: Tier 0 is based only on the available measurements and therefore does not completely cover the whole globe or the full vertical range uniformly; the Tier 0.5 monthly mean zonal means are calculated as a filled version of the Tier 0 database where missing monthly mean zonal mean values are estimated from correlations against a total column ozone (TCO) database. The Tier 0.5 data set includes the full range of measurement variability and is created as an intermediate step for the calculation of the Tier 1 data where a least squares regression model is used to attribute variability to various known forcing factors for ozone. Regression model fit coefficients are expanded in Fourier series and Legendre polynomials (to account for seasonality and latitudinal structure, respectively). Four different combinations of contributions from selected regression model basis functions result in four different Tier 1 data sets that can be used for comparisons with chemistry-climate model (CCM) simulations that do not exhibit the same unforced variability as reality (unless they are nudged towards reanalyses). Compared to previous versions of the database, this update includes additional satellite data sources and ozonesonde measurements to extend the database period to 2016. Additional improvements over the previous version of the database include the following: (i) adjustments of measurements to account for biases and drifts between different data sources (using a chemistry-transport model, CTM, simulation as a transfer standard), (ii) a more objective way to determine the optimum number of Fourier and Legendre expansions for the basis function fit coefficients, and (iii) the derivation of methodological and measurement uncertainties on each database value are traced through all data modification steps. Comparisons with the ozone database from SWOOSH (Stratospheric Water and OzOne Satellite Homogenized data set) show good agreement in many regions of the globe. Minor differences are caused by different bias adjustment procedures for the two databases. However, compared to SWOOSH, BSVertOzone additionally covers the troposphere. Version 1.0 of BSVertOzone is publicly available at https://doi.org/http://doi.org/10

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Section: Introductionmentioning

confidence: 99%

An updated version of a gap-free monthly mean zonal mean ozone database

Haßler

Kremser

Bodeker

et al. 2018

Earth Syst. Sci. Data

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“…The methodology to generate the Tier 1.1 to Tier 1.4 data sets is much the same approach as the one described for previous version of the database (BDBP v1.1.0.6) in Bodeker et al (2013). The same regression model is applied, but rather than using 20 this regression model to both fill the data gaps and conduct the attribution, it is now only used to conduct the attribution to various factors affecting ozone.…”

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confidence: 99%

“…For an in depth description of the approach see Sect. 4 of Bodeker et al (2013). The most important features of the used approach are briefly outlined below.…”

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confidence: 99%

“…As for the BDBP, BSVertOzone provides different Tier data sets (Bodeker et al, 2013): There are several improvements that could be implemented when preparing the measurements and for the used homogenization method. In the current version (v1.0) of BSVertOzone, the global troposphere is only covered by ozonesonde profile measurements.…”

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confidence: 99%

“…The uncertainties on the corrections applied are included in the total uncertainty for each individual data point. For the final calculation of monthly mean zonal means values, additional data filtering was applied, similar to the filtering described in Bodeker et al (2013), e.g. SAGE II data were not used below 10 km (below 242.8hPa for the pressure grid), and SAGE I data were not used below 18km (77.44hPa).…”

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An updated version of a gap-free monthly mean zonal mean ozone database

Haßler¹,

Kremser²,

Bodeker³

et al. 2018

Preprint

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Modeling the climate impact of Southern Hemisphere ozone depletion: The importance of the ozone data set

Young

Davis

Haßler

et al. 2014

Geophysical Research Letters

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The ozone hole is an important driver of recent Southern Hemisphere (SH) climate change, and capturing these changes is a goal of climate modeling. Most climate models are driven by off-line ozone data sets. Previous studies have shown that there is a substantial range in estimates of SH ozone depletion, but the implications of this range have not been examined systematically. We use a climate model to evaluate the difference between using the ozone forcing (Stratospheric Processes and their Role in Climate (SPARC)) used by many Intergovernmental Panel on Climate Change Fifth Assessment Report (Coupled Model Intercomparison Project) models and one at the upper end of the observed depletion estimates (Binary Database of Profiles (BDBP)). In the stratosphere, we find that austral spring/summer polar cap cooling, geopotential height decreases, and zonal wind increases in the BDBP simulations are all doubled compared to the SPARC simulations, while tropospheric responses are 20-100% larger. These results are important for studies attempting to diagnose the climate fingerprints of ozone depletion.

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

Cited by 58 publications

References 35 publications

An updated version of a gap-free monthly mean zonal mean ozone database

An updated version of a gap-free monthly mean zonal mean ozone database

An updated version of a gap-free monthly mean zonal mean ozone database

Modeling the climate impact of Southern Hemisphere ozone depletion: The importance of the ozone data set

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