Abstract
We conducted a comparison of trends in lower stratospheric temperatures and summer zonal wind fields based on 27 years of reanalysis data and output from hindcast simulations using a coupled ocean- atmospheric general circulation model (OAGCM). Lower stratospheric ozone in the OAGCM was relaxed to the observed climatology and increasing greenhouse gas concentrations were neglected. In the reanalysis, lower stratospheric ozone fields were better represented than in the OAGCM. The spring lower stratospheric/ upper tropospheric cooling in the polar cap observed in the reanalysis, which is caused by a direct ozone depletion in the past two decades and is in agreement with previous studies, did not appear in the OAGCM. The corresponding summer tropospheric response also differed between data sets. In the reanalysis, a statistically significant poleward trend of the summer jet position was found, whereas no such trend was found in the OAGCM. Furthermore, the jet position in the reanalysis exhibited larger interannual variability than that in the OAGCM. We conclude that these differences are caused by the absence of long-term lower stratospheric ozone changes in the OAGCM. Improper representation or non-inclusion of such ozone variability in a prediction model could adversely affect the accuracy of the predictability of summer rainfall forecasts over South Africa.
Highlights
The El Nino Southern Oscillation (ENSO) phenomenon is the single biggest contributing factor to climate variability because of its large global impact.[1]
We propose that an OAGCM whose model top is only at 10 hPa would likely be incapable of capturing all the dynamics associated with the variability of the polar vortex
The efforts of improving our understanding of the coupled system through modelling and predictability studies should include the knowledge of stratospheric as well as chemical processes (e.g. CO2 and ozone) which contribute to the so-called ‘complete climate system’. This notion was endorsed by the World Climate Research Programme's (WCRP) Climate Variability and Predictability (CLIVAR) in aiming to improve climate and intra-seasonal predictability.[54]
Summary
AUTHORS: Kelebogile Mathole[1] Thando Ndarana[1] Asmerom Beraki[1] Willem A. Landman[2,3]. AFFILIATIONS: 1South African Weather Service – Research, Pretoria, South Africa 2Council for Scientific and Industrial Research – Natural Resources and the Environment, Pretoria, South Africa 3Department of Geography, Geoinformatics and Meteorology, University of Pretoria, Pretoria, South Africa
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