The impact of solar variability on climatic parameters

Natural climate variability is partially attributed to the solar radiative forcing. The scope of this work is to increase the scientific understanding of the relative role of solar variations on the terrestrial climate. The applied methodology examines initially the variation of multiple climatic parameters (temperature, zonal wind, relative and specific humidity, sensible and latent surface heat flux, cloud cover, precipitation) in response to the 11-year solar cycle. An additional goal is to estimate the response of the climate system’s parameters to the solar forcing in multiple forecasting horizons and to evaluate the behavior of the climate system in shorter time scales. The adopted methodology includes the development of linear regression models which calculate the dependency of the climatic parameters to solar variations for each grid point of the global dataset on a monthly time scale. The solar indicator used in this study is the 10.7-cm solar radio flux (F10.7) provided by NOAA, while the climate data are extracted from the NCEP/NCAR Reanalysis 1 project with a spatial resolution of 2.5^o X 2.5^o for 67 years. Regarding the climate system’s response forecasting, an Artificial Neural Network has been trained for modeling and forecasting the solar indicator time series for a few time steps in advance and the effect on climatic parameters is estimated using the established regression equations. The results exhibit that the variation of the climatic parameters can be partially attributed to the 11-year solar cycle. Statistically significant areas with relatively high solar cycle signal were found in multiple pressure levels and geographical regions. Furthermore, the results indicate that the identification of a clear solar signal in the climatic data is a difficult task due to the climate system’s complexity; advanced non-linear methods could be applied in order to obtain a more accurate understanding of this research field.