The influence of solar variability and the quasi-biennial oscillation on lower atmospheric temperatures and sea level pressure

Abstract

Our fundamental aim is to investigate solar cycle signals in sea level pressure. In order to see if these may relate, especially at high latitudes, to the solar influence on the stratosphere we start by investigating the temperature of the winter polar stratosphere and its dependence on the state of the Sun and the phase of the Quasi-Biennial Oscillation (QBO). We find that the choice of pressure level used to define the phase of the QBO is important in determining how the solar and QBO influences appear to act in combination. Informed by this we carry out a multiple linear regression analysis of zonal mean temperatures throughout the lower stratosphere and troposphere. A combined solar*QBO temporal index exhibits strongly in the lower stratosphere, but in much of the troposphere any influence of the QBO, either on its own or coupled to solar effects is much smaller than the pure solar signal. We use a similar approach to analyse sea level pressure (SLP) data, first using a standard QBO time series dating back to 1953. We find at high latitudes that individually the solar and QBO signals are weak but that the compound solar*QBO temporal index shows a significant signal. This is such that combinations of low solar activity with westerly QBO and high solar activity with easterly QBO are both associated with a strengthening in the polar modes; while the opposite combinations coincide with a weakening. By employing a QBO dataset reconstructed back to 1900, we extend the SLP analysis back to that date and also find a robust signal in the surface SAM; though weaker for surface NAM. Our results suggest that solar variability, modulated by the phase of QBO, influences zonal mean temperatures at high latitudes in the lower stratosphere, in the mid-latitude troposphere and sea level pressure near the poles. Thus a knowledge of the state of the Sun, and the phase of the QBO might be useful in surface climate prediction. © Author(s) 2011.