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Alexey Yu. Karpechko, Douglas Maraun, and Veronika Eyring

Abstract

Accurate projections of stratospheric ozone are required because ozone changes affect exposure to ultraviolet radiation and tropospheric climate. Unweighted multimodel ensemble-mean (uMMM) projections from chemistry–climate models (CCMs) are commonly used to project ozone in the twenty-first century, when ozone-depleting substances are expected to decline and greenhouse gases are expected to rise. Here, the authors address the question of whether Antarctic total column ozone projections in October given by the uMMM of CCM simulations can be improved by using a process-oriented multiple diagnostic ensemble regression (MDER) method. This method is based on the correlation between simulated future ozone and selected key processes relevant for stratospheric ozone under present-day conditions. The regression model is built using an algorithm that selects those process-oriented diagnostics that explain a significant fraction of the spread in the projected ozone among the CCMs. The regression model with observed diagnostics is then used to predict future ozone and associated uncertainty. The precision of the authors’ method is tested in a pseudoreality; that is, the prediction is validated against an independent CCM projection used to replace unavailable future observations. The tests show that MDER has higher precision than uMMM, suggesting an improvement in the estimate of future Antarctic ozone. The authors’ method projects that Antarctic total ozone will return to 1980 values at around 2055 with the 95% prediction interval ranging from 2035 to 2080. This reduces the range of return dates across the ensemble of CCMs by about a decade and suggests that the earliest simulated return dates are unlikely.

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Sabrina Wenzel, Veronika Eyring, Edwin P. Gerber, and Alexey Yu. Karpechko

Abstract

Stratospheric ozone recovery and increasing greenhouse gases are anticipated to have a large impact on the Southern Hemisphere extratropical circulation, shifting the jet stream and associated storm tracks. Models participating in phase 5 of the Coupled Model Intercomparison Project poorly simulate the austral jet, with a mean equatorward bias and 10° latitude spread in their historical climatologies, and project a wide range of future trends in response to anthropogenic forcing in the representative concentration pathway (RCP) scenarios. Here, the question is addressed whether the unweighted multimodel mean (uMMM) austral jet projection of the RCP4.5 scenario can be improved by applying a process-oriented multiple diagnostic ensemble regression (MDER). MDER links future projections of the jet position to processes relevant to its simulation under present-day conditions. MDER is first targeted to constrain near-term (2015–34) projections of the austral jet position and selects the historical jet position as the most important of 20 diagnostics. The method essentially recognizes the equatorward bias in the past jet position and provides a bias correction of about 1.5° latitude southward to future projections. When the target horizon is extended to midcentury (2040–59), the method also recognizes that lower-stratospheric temperature trends over Antarctica, a proxy for the intensity of ozone depletion, provide additional information that can be used to reduce uncertainty in the ensemble mean projection. MDER does not substantially alter the uMMM long-term position in jet position but reduces the uncertainty in the ensemble mean projection. This result suggests that accurate observational constraints on upper-tropospheric and lower-stratospheric temperature trends are needed to constrain projections of the austral jet position.

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Robert Ferraro, Duane E. Waliser, Peter Gleckler, Karl E. Taylor, and Veronika Eyring
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