The potential to narrow uncertainty in projections of stratospheric ozone over the 21st century

Abstract

Abstract. Future stratospheric ozone concentrations will be determined both by changes in the concentration of ozone depleting substances (ODSs) and by changes in stratospheric and tropospheric climate, including those caused by changes in anthropogenic greenhouse gases (GHGs). Since future economic development pathways and resultant emissions of GHGs are uncertain, anthropogenic climate change could be a significant source of uncertainty for future projections of stratospheric ozone. In this pilot study, using an "ensemCorrespondence to: A. J. Charlton-Perez (a.j.charlton@reading.ac.uk) ble of opportunity" of chemistry-climate model (CCM) simulations, the contribution of scenario uncertainty from different plausible emissions pathways for ODSs and GHGs to future ozone projections is quantified relative to the contribution from model uncertainty and internal variability of the chemistry-climate system. For both the global, annual mean ozone concentration and for ozone in specific geographical regions, differences between CCMs are the dominant source of uncertainty for the first two-thirds of the 21st century, upto and after the time when ozone concentrations return to 1980 values. In the last third of the 21st century, dependent upon the set of greenhouse gas scenarios used, scenario uncertainty can be the dominant contributor. This result Published by Copernicus Publications on behalf of the European Geosciences Union. suggests that investment in chemistry-climate modelling is likely to continue to refine projections of stratospheric ozone and estimates of the return of stratospheric ozone concentrations to pre-1980 levels.

Comparisons of polar processing diagnostics from 34 years of the ERA-Interim and MERRA reanalyses

“…While some studies have linked changes in V PSC to an expectation of colder winters and greater ozone loss in the Arctic to global climate change (Rex et al, 2004, others do not support this conclusion (Hitchcock et al, 2009;Pommereau et al, 2013;Rieder and Polvani, 2013). Climate model projections of future ozone loss are also highly uncertain (e.g., Charlton-Perez et al, 2010). Thus, the prediction of future ozone loss is still problematic, and improvements in such predictions will require better understanding of the uncertainties and potential biases in representation of the meteorological conditions upon which polar processing depends so critically in commonly used DAS.…”

Stratospheric ozone change and related climate impacts over 1850–2100 as modelled by the ACCMIP ensemble

“…6a). Natural variability in models not constrained by observed meteorology is difficult to reproduce (Austin et al, 2003;Charlton-Perez et al, 2010Butchart et al, 2011;Shepherd et al, 2014) such as, the abnormally cold boreal winters in the mid-1990s (i.e. more PSCs formation), which resulted in enhanced ozone loss during boreal spring (Newman et al, 2001).…”

Role of external factors in the evolution of the ozone layer and stratospheric circulation in 21st century

“…It was mentioned in several publications (e.g., Eyring et al, 2010;Charlton-Perez et al, 2010;Strahan et al, 2011) that there is a substantial scatter among CCMs in the projection of the future ozone layer, however, the reasons for this uncertainty have not been clearly identified. As it was shown by Charlton-Perez et al (2010), the ambiguity of the future ozone evolution depends mainly on the model and scenario uncertainties, while the contribution of the internal model variability is small. There is no strong evidence whether this is caused by some model deficiencies in the representation of the chemical, dynamical and transport processes or by the differences in the prescribed external forcing.…”

“…The application of different SST/SI data sets to estimate the uncertainty of the future ozone behavior is important. But it is also important to understand and attribute the causes of the uncertainty (e.g., Charlton-Perez et al, 2010). For the CCM community this is a crucial problem, because if the prescribed SST/SI is responsible for large scatter among model predictions any model improvements would not help to reduce the scatter.…”