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combining a number of different satellite-based instruments (Bodeker et al., 2005;
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Struthers et al., 2009), and observations from the Solar Backscatter Ultraviolet
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(SBUV - version 8.6) merged ozone data set (McPeters et al., 2013). In addition,
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Figure 1 includes trends from the IGAC/SPARC ozone data set (Cionni et al., 2011)
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which was used by the majority of the models with prescribed ozone concentrations
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(both ACCMIP and CMIP5). The annual mean is used in evaluations for the global,
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tropical and midlatitudes regions. Additional evaluations are made for the boreal
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spring in the Arctic (March, April and May) and the austral spring in the Antarctic
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(September, October and November) when strongest ozone depletion occurs.
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Within uncertainty, the overall response for ACCMIP is in good agreement with
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observational data sets in terms of decadal trends and absolute values, with the
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Northern Hemisphere (NH) being the region where models differ most. These results
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also compare favourably with those reported by WMO (2014). In line with CMIP5
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and CCMVal2 models, strongest changes are found over Antarctica in austral spring
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associated to the ozone hole, and smallest over the tropics where ODS are least
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effective. ACCMIP NOCHEM models typically simulate smaller decadal trends than
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CHEM models, consistent with the possible underestimation of SH ozone depletion
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trends in the IGAC/SPARC ozone data set (Hassler et al., 2013; Young et al., 2014).
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However, outside extratropical SH regions, IGAC/SPARC ozone data set (i.e. used to
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drive the majority of ACCMIP and CMIP5 NOCHEM models) tends to show better
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agreement with observations than CHEM models. ACCMIP CHEM and CMIP5
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CHEM models show very similar TCO decadal trends in all regions (± 0.1⎯0.2 % dec-
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depletion is greatest (± 2.9 % dec-1). ACCMIP NOCHEM and CMIP5 NOCHEM
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models show more disparate trends (± 0.5⎯2.1 % dec-1), which may be related to
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different ozone data sets and the implementation method on each model (i.e. online
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tropospheric chemistry in ACCMIP models).
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Figure 2 compares vertically resolved ozone decadal trends for the same period,
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regions and seasons, for the ACCMIP multi-model mean and individual models
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against the Binary Database of Profiles (BDBP version 1.1.0.6) data set, using the so-
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called Tier 0 and Tier 1.4 data (Bodeker et al., 2013). Tier 0 includes ozone
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measurements from a wide range of satellite and ground-based platforms, whereas
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Tier 1.4 is a regression model fitted to the same observations. Uncertainty estimates
), although differing somewhat more at high latitudes in the SH, where ozone
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