Climate dynamics

Abstract:

The interaction of the 11-yr solar cycle (SC) and the quasi-biennial oscillation (QBO) and their influence on the Northern Hemispbere (NH) polar vortex are studied using idealized model experiments and ECMWF Re-Analysis (ERA-40). In the model experiments, the sensitivity of the NH polar vortex to imposed easterlies at equatorial/subtropical latitudes over various height ranges is tested to explore the possible influence from zonal wind anomalies associated with the QBO and the 11-yr SC in those regions. The experiments show that the timing of the modeled stratospheric sudden warmings (SSWs) is sensitive to the imposed easterlies at the equator/subtropics. When easterlies are imposed in the equatorial or subtropical upper stratosphere, the onset of the SSWs is earlier. A mechanism is proposed in which zonal wind anomalies in the equatorial/subtropical upper stratosphere associated with the QBO and 11-yr SC either reinforce each other or cancel each other out. When they reinforce, as in Smin-QBO-east (Smin/E) and S max-QBO-west (Smax/W), it is suggested that the resulting anomaly is large enough to influence the development of the Aleutian high and hence the time of onset of the SSWs. Although highly speculative, this mechanism may help to understand the puzzling observations that major warmings often occur in Smax/W years even though there is no strong waveguide provided by the QBO winds in the lower equatorial stratosphere. The ERA-40 data are used to investigate the QBO and solar signals and to determine whether the observations support the proposed mechanism. Composites of ERA-40 zonally averaged zonal winds based on the QBO (E/W), the SC (min/max), and both (Smin/E, Smin/W, Smax/E, S max/W) are examined, with emphasis on the Northern Hemisphere winter vortex evolution. The major findings are that QBO/E years are more disturbed than QBO/W years, primarily during early winter. Sudden warmings in Smax years tend to occur later than in Smin years. Midwinter warmings are more likely during Smin/E and Smax/W years, although the latter result is only barely statistically significant at the 75% level. The data show some support for the proposed mechanism, but many more years are required before it can be fully tested. © 2004 American Meteorological Society.

Abstract:

This work is focused on the application of neural network based models to the analysis of total ozone (TO) time series. Processes that affect total ozone are extremely non linear, especially at the considered European mid-latitudes. Artificial neural networks (ANNs) are intrinsically non-linear systems, hence they are expected to cope with TO series better than classical statistics do. Moreover, neural networks do not assume the stationarity of the data series so they are also able to follow time-changing situations among the implicated variables. These two features turn NNs into a promising tool to catch the interactions between atmospheric variables, and therefore to extract as much information as possible from the available data in order to make, for example, time series reconstructions or future predictions. Models based on NNs have also proved to be very suitable for the treatment of missing values within the data series. In this paper we present several models based on neural networks to fill the missing periods of data within a total ozone time series, and models able to reconstruct the data series. The results released by the ANNs have been compared with those obtained by using classical statistics methods, and better accuracy has been achieved with the non linear ANNs techniques. Different network structures and training strategies have been tested depending on the specific task to be accomplished. © European Geosciences Union 2004.