Atmospheric models are used to interpret constituent observations and to predict the response of ozone to changes in composition, including the changes in stratospheric chlorine that have taken place due to release of man-made ozone depleting substances (ODSs). The Montreal Protocol and its amendments banned the production of many of these compounds beginning in 1996, and surface measurements of chlorofluorcarbons CFCl3 and CF2Cl2 show that their atmospheric concentrations leveled off and began to decrease after the late 1990s [Daniel and Velders et al., 2007]. The effects of ODSs are expected to be easiest to quantify in the upper stratosphere where photochemical processes control the ozone level. First efforts to identify the atmospheric response to the Montreal Protocol have focused on the upper stratosphere, and Newchurch et al.  reported evidence that the upper stratospheric ozone had ceased to decline. Presently upper stratospheric ozone is expected to increase both because of the decline in ODSs and because greenhouse gases continue to increase, cooling the stratosphere and decreasing the rate of catalytic ozone destruction as noted in the Scientific Assessment of Ozone Depletion: 2010 [WMO, 2011; hereafter referred to as WMO2011]. Attribution of observed changes in ozone to changes in ODSs requires untangling the effects of ODSs from the effects of continuing increases in greenhouse gases [Douglass and Fioletov et al., 2011].
 Projections of future ozone levels are now commonly made using models that couple a general circulation model (GCM) with a representation of atmospheric photochemical processes, allowing interactions among photochemical processes, radiation, and dynamics. Such models are known as coupled chemistry-climate models (CCMs) and were evaluated by the Stratospheric Processes and their Role in Climate (SPARC) -sponsored CCM validation (CCMVal) activity. Performance metrics related to model representation of processes identified in observations were agreed upon in a series of workshops. TheSPARC Report on the Evaluation of Chemistry-Climate Models [SPARC CCMVal, 2010] describes in detail the successes and deficiencies of participating models. These models contributed simulations to WMO2011. Oman et al.  analyzed the various projections using multiple linear regression (MLR) and reported broad agreement among models in that simulated ozone principally responds to two forcings: 1) prescribed surface mixing ratios for chlorine and bromine containing source gases that change the stratospheric amounts of chlorine and bromine; and 2) prescribed boundary conditions for greenhouse gases that result in stratospheric cooling. In spite of broad agreement, ozone sensitivity to chlorine change varies among models throughout the stratosphere. It is not surprising that model responses vary in the middle and lower stratosphere, where photochemical time scales become long and both photochemical and transport changes contribute to ozone change. However, even in the upper stratosphere where photochemical processes dominate, the computed ozone percentage changes, the year that the ozone mixing ratio returns to 1980 values, and the sensitivity of ozone to perturbations in chlorine and temperature vary among models.
 Inspired by the CCMVal exercises, increased attention is being given to application of performance metrics and best use of the wealth of observational information obtained from satellites, including instruments on the National Aeronautics and Space Administration (NASA) Upper Atmosphere Research Satellite (UARS) [Reber et al., 1993], European Space Agency Environmental Satellite (Envisat), the Canadian Scientific Satellite (SciSat) [Bernath et al., 2005] and the NASA satellite Aura [Schoeberl et al., 2006], in order to arrive at the best projection for twenty-first-century ozone.Waugh and Eyring assigned weights to projections of twenty-first-century total column ozone based on a set of performance metrics that quantify model representation of processes thought to be key to ozone evolution. In theWaugh and Eyring  study the weighted mean was nearly the same as the unweighted mean of all of the models that participated in the exercise, in spite of obvious differences and deficiencies among models' representation of several important stratospheric processes. Strahan et al. focused on the transport evaluation of CCMVal-2 participant models, identifying four models with the most realistic transport that also performed well on the chemistry evaluation. The projections for total column ozone from these four models are more similar to each other than the ensemble of projections, and more similar than random selection of four simulations from the group of projections. The analysis byStrahan et al.  identifies the models with credible transport but did not demonstrate a direct relationship between transport deficiencies and the rate of recovery for stratospheric ozone. In some models, deficiencies not directly related to transport affect simulated ozone recovery. Deficiencies that are identified in the CCMVal chemistry evaluation [SPARC CCMVal, 2010, chap. 6] include problems with partitioning among chlorine species, missing chemical reactions important to chlorine chemistry and lack of conservation for the total amount of chlorine released from source gases whose mixing ratios are specified at the lower boundary.
 Similar physical concepts underlie the GCMs and photochemical representations that comprise the CCMVal-2 models. We note that the CCMVal chemistry evaluation states that all of the models contain a description of main chemical species of relevance for stratospheric ozone [SPARC CCMVal, 2010, chap. 6]. Because photochemical representations are similar, understanding why projections of ozone recovery differ is a step toward higher confidence in predictions. This paper focuses on the upper stratosphere, where the photochemical lifetimes of ozone, fluorocarbons, and other gases like N2O are short, and the transport contribution to the ozone continuity equation is negligible. This paper relies on the well-developed conceptual model for the photochemical processes that control ozone as described below, focusing on the relationship between upper stratospheric ozone and temperature. Our focus on upper stratospheric ozone and temperature follows from previous theoretical studies and analysis of observations.Stolarski and Douglass  developed a parameterization that shows how the sensitivity of ozone to temperature that is due to the temperature dependence of ozone loss varies depending on the relative contributions of recombination of atomic oxygen and ozone and the other catalytic cycles to net ozone loss. Douglass and Rood  analyzed ozone and temperature observations obtained from the Limb Infrared Monitor of the Stratosphere (LIMS) experiment on Nimbus 7 [Gille and Russell, 1984], concluding that ozone would become less sensitive to temperature as chlorine increased. Chandra et al. reported a 10%–25% per decade decrease in the amplitude of the ozone seasonal cycle at 2 hPa middle latitudes in both hemispheres due to chlorine increase using ozone observations from 1979–1993. We show below although the upper stratospheric ozone sensitivity to temperature as quantified for each CCMVal-2 model decreased as anthropogenic chlorine increased and will increase as chlorine returns to unperturbed levels, there are quantitative differences in the sensitivity of simulated ozone to temperature. We investigate differences in the ozone sensitivity to temperature as obtained from the different CCMVal-2 models, focusing on their cause. Our intent is to show how this relationship and its behavior in the past and present atmosphere provide insight into the differences in predicted upper stratospheric ozone levels in the twenty-first century [Bekki and Bodeker et al., 2011].
 We present the conceptual model for upper stratospheric ozone in Section 2. Section 3 describes the CCMVal models (listed in Table 1) and the simulations that are analyzed using this conceptual model. Results are given in section 4, with discussion and conclusions in section 5.
|AMTRAC3||Austin and Wilson |
|CCSRNIES||Akiyoshi et al. |
|CMAM||Scinocca et al. ; de Grandpré et al. |
|CNRM-ACM||Déqué ; Teyssèdre et al. |
|GEOSCCM||Pawson et al. |
|LMDZrepro||Jourdain et al. |
|MRI||Shibata and Deushi [2008a, 2008b]|
|Niwa-SOCOL||Schraner et al. |
|SOCOL||Schraner et al. |
|ULAQ||Pitari et al. |
|UMSLIMCAT||Tian and Chipperfield ; Tian et al. |
|UMUKCA-METO||Davies et al. ; Morgenstern et al. |
|UMUKCA-UCAM||Davies et al. ; Morgenstern et al. |
|WACCM||Garcia et al. |