Περίληψη:
Natural climate variability is partially attributed to solar radiative
forcing. The purpose of this study is to contribute to a better
understanding of the influence of solar variability on the Earth's
climate system. The object of this work is the estimation of the
variation of multiple climatic parameters (temperature, zonal wind,
relative and specific humidity, sensible and latent surface heat flux,
cloud cover and precipitable water) in response to solar cycle forcing.
An additional goal is to estimate the response of the climate system's
parameters to short-term solar variability in multiple forecasting
horizons and to evaluate the behavior of the climate system in shorter
time scales. The solar cycle is represented by the 10.7 cm solar flux, a
measurement collected by terrestrial radio telescopes, and is provided
by NOAA/NCEI/STP, whereas the climatic data are provided by the
NCEP/NCAR reanalysis 1 project. The adopted methodology includes the
development of a linear regression statistical model in order to
calculate the climatic parameters' feedback to the 11-year solar cycle
on a monthly scale. Artificial Neural Networks (ANNs) have been employed
to forecast the solar indicator time series for up to 6 months in
advance. The climate system's response is further forecasted using the
ANN's estimated values and the regression equations. The results show
that the variation of the climatic parameters can be partially
attributed to solar variability. The solar-induced variation of each of
the selected parameters, averaged globally, was of an order of magnitude
of 10(-1)-10(-3), and the corresponding correlation coefficients
(Pearson's r) were relatively low (-0.5-0.5). Statistically significant
areas with relatively high solar cycle signals were found at multiple
pressure levels and geographical areas, which can be attributed to
various mechanisms.
Συγγραφείς:
Tzanis, Chris G.
Benetatos, Charilaos
Philippopoulos, Kostas
