Unit:
Κατεύθυνση Φυσική ΠεριβάλλοντοςLibrary of the School of Science
Author:
Benetatos Charilaos
Supervisors info:
Χρήστος Τζάνης, Επίκουρος Καθηγητής, Τμήμα Φυσικής, Ε.Κ.Π.Α.
Κωσταντίνος Βαρώτσος, Καθηγητής, Τμήμα Φυσικής, Ε.Κ.Π.Α.
Κωσταντίνος Ελευθεράτος, Επίκουρος Καθηγητής, Τμήμα Γεωλογίας και Γεωπεριβάλλοντος, Ε.Κ.Π.Α.
Original Title:
Η συνεισφορά του ηλιακού κύκλου στη διακύμανση παραμέτρων του κλιματικού συστήματος
Translated title:
The contribution of the solar cycle in the variation of multiple climatic parameters
Summary:
The subject of this MSc thesis is to contribute to a better understanding of the influence of solar variability on earth’s climate system. In order to achieve this, the purpose of this thesis 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 precipitation) in response to the solar cycle forcing. The primary objective is to quantify the maximum response of the climate system to the 11-year solar cycle. Specifically, the differences between solar maximum and minimum activity results in a variation of approximately 0.1 % (~ 1 W/m2) in Total Solar Irradiance (TSI) and up to several percent in the UV part of Solar Spectral Irradiance (SSI), which are inducing variations in these climatic parameters. An additional goal is to forecast the response of the climate system’s parameters to the solar cycle in multiple forecasting horizons, in order to evaluate the behavior of the climate system in shorter time ranges.
The applied methodology includes the development of a statistical linear regression model which calculates the dependency of the climatic parameters to the solar activity variation for every point of the global data grid. This dependency is expressed by the regression coefficients and by using the regression equations the climatic parameter values can be estimated for every point in the data grid and for every month. Subsequently, the estimated values of the climatic parameters in the solar maximum and minimum years are isolated and averaged correspondingly. Finally, the numerical difference between the averaged climatic parameters in solar maximum and solar minimum conditions is calculated. 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. The forecasted solar data series can be inserted in the previously mentioned regression equations and thus the numerical differences in the climatic parameters can be calculated anew in a different time range. The new time range is equal to a few months (the time length of the new solar data series) and this process results in the prediction of the climatic parameters’ response to the solar cycle for some time steps into the future.
The results show that the variation of the climatic parameters can be partially attributed to the solar cycle. The correlation coefficients were relatively low (-0.5 < C.C. < 0.5) and the solar induced response of all the selected parameters, averaged globally, was of order 10-1 - 10-3. Statistically significant areas (> 95 %) with relatively high solar cycle signal were found in multiple pressure levels and geographical areas which can be attributed to multiple mechanisms.
One of the mechanisms involves the increase of stratospheric ozone concentrations because of the higher UV output by the sun and the increase of the stratospheric temperature due to the increased stratospheric ozone concentrations. Moreover, significant responses are found in the subtropical zonal wind and the explanation possibly involves the interaction of planetary waves with the mean circulation flow. Finally, the results in the remaining climatic parameters can be connected to the ‘’Bottom-up’’ mechanism. In the relevant study, it was stated that higher solar radiation absorption by the subtropical sea surface can result in stronger trade winds and Hadley and Walker circulation through the processes of evaporation and the associated upward vertical motions. This strengthened circulation results in the enhancement of subtropical subsidence which reduces cloud cover and further increases radiative forcing at the surface. To conclude, It should be noted that it is difficult to obtain a clear solar cycle signal in the climate parameters due the climate system’s complexity, thus different methods must be applied in order to obtain a more accurate understanding of this complex research field.
Main subject category:
Science
Keywords:
Solar Cycle, Climate Parameter, Statistical Model, Artificial Neural Network, Forecast Horizon.
