GEOMAGNETIC DATA AS A SOURCE OF INFORMATION ON PAST EVOLUTION OF THE SOLAR ACTIVITY/SPACE CLIMATE

Book Chapter in INSIGHTS OF GEOSCIENCES FOR NATURAL HAZARDS AND CULTURAL HERITAGE, Editor: Florina CHITE

On the response of the European climate to solar/geomagnetic long-term activity

The response of the European climate to long-term solar/geomagnetic activity is investigated using surface-air temperature and solar/geomagnetic indices. A set of 21 time series of air temperatures measured at European stations between 1900 and 2006, and 4 European and 14 Romanian stations with 150-year-long records, were used. Strong and coherent solar signals were found at Schwabe and Hale solar-cycle timescales, with peak-to-trough amplitudes of several degrees, and 0.6 ˚C to 0.8 ˚C, respectively. Interdecadal and centennial trends as defined by 11-year and 22-year running averages, respectively, of the annual mean time series differ significantly from corresponding trends in solar/geomagnetic activity, which indicates the presence of temperature variations at a 40-year timescale that are possibly related to the internal dynamics of the atmospheric system. The data show similar temporal behaviors at all of the stations analyzed, with amplitude differences that can be understood in terms of large-scale atmospheric circulation patterns that are influenced by the solar/geomagnetic forcing at the corresponding timescales, although with local intensity differences

Solar Signature in Climate Indices

The influence of solar/geomagnetic activity on climate variables still remains a fully unclarified problem, although many scientific efforts have been made to better understand it. In order to bring more information to this open problem, in the present study, we analyze the connection between solar/geomagnetic activity (predictors) and climate variables (predictands) by applying elements from information theory and wavelet transform analysis. The solar activity was highlighted by the Wolf number and geomagnetic activity was quantified by the aa index. For the climate variables, we considered seven Climate Indices (CIs) that influence atmospheric circulation on regional or global scales, such as the Greenland-Balkan Oscillation Index (GBOI), North Atlantic Oscillation Index (NAOI), Arctic Oscillation (AO), Atlantic Multidecadal Oscillation (AMO), Southern Oscillation Index (SOI), Bivariate ENSO Timeseries (BEST) and Trans-Niño Index (TNI). By using the difference between synergy and redundancy, a few cases were found where the two predictors can be considered together for CIs’ estimation. Coherence analysis through the wavelet transform for three variables, both through multiple and partial analysis, provides the time intervals and bands of periods, where the two considered predictors can be used together or separately. The results differ depending on the predictand, the season and the considered lags. Significant information is brought out by using the two predictors together, namely the summer season, for GBOI and NAOI, when the predictors were taken 2 years before, and the winter season, as AMO responds to the variations of both solar and geomagnetic activity after 4 years

Selection of Optimal Palmer Predictors for Increasing the Predictability of the Danube Discharge: New Findings Based on Information Theory and Partial Wavelet Coherence Analysis

The purpose of this study was to obtain synergistic information and details in the time–frequency domain of the relationships between the Palmer drought indices in the upper and middle Danube River basin and the discharge (Q) in the lower basin. Four indices were considered: the Palmer drought severity index (PDSI), Palmer hydrological drought index (PHDI), weighted PDSI (WPLM) and Palmer Z-index (ZIND). These indices were quantified through the first principal component (PC1) analysis of empirical orthogonal function (EOF) decomposition, which was obtained from hydro-meteorological parameters at 15 stations located along the Danube River basin. The influences of these indices on the Danube discharge were tested, both simultaneously and with certain lags, via linear and nonlinear methods applying the elements of information theory. Linear connections were generally obtained for synchronous links in the same season, and nonlinear ones for the predictors considered with certain lags (in advance) compared to the discharge predictand. The redundancy–synergy index was also considered to eliminate redundant predictors. Few cases were obtained in which all four predictors could be considered together to establish a significant information base for the discharge evolution. In the fall season, nonstationarity was tested through wavelet analysis applied for the multivariate case, using partial wavelet coherence (pwc). The results differed, depending on the predictor kept in pwc, and on those excluded

On the crustal bias of repeat stations in Romania

A magnetic induction model has been applied to recordings obtained in 2010 during the field campaigns for geomagnetic measurements at the 26 repeat stations of the Romanian secular variation network. The model is based on the observation that a variable external magnetic field induces a response of the Earth's interior not only by electromagnetic induction, but also by magnetic induction in the magnetic rocks above the Curie temperature. The model computes coefficients of a linear relationship between recorded values of a certain geomagnetic element (X, Y, Z, or F) at the repeat station and recorded X, Y, Z values at a reference station (in this case, SUA observatory). Coefficients depend on magnetic permeabilities of rocks beneath the station and stand as a proxy for the anomaly bias characterizing the site. Maps of the lateral variation of this type of information were obtained and discussed

Solar Signature in Climate Indices

The influence of solar/geomagnetic activity on climate variables still remains a fully unclarified problem, although many scientific efforts have been made to better understand it. In order to bring more information to this open problem, in the present study, we analyze the connection between solar/geomagnetic activity (predictors) and climate variables (predictands) by applying elements from information theory and wavelet transform analysis. The solar activity was highlighted by the Wolf number and geomagnetic activity was quantified by the aa index. For the climate variables, we considered seven Climate Indices (CIs) that influence atmospheric circulation on regional or global scales, such as the Greenland-Balkan Oscillation Index (GBOI), North Atlantic Oscillation Index (NAOI), Arctic Oscillation (AO), Atlantic Multidecadal Oscillation (AMO), Southern Oscillation Index (SOI), Bivariate ENSO Timeseries (BEST) and Trans-Niño Index (TNI). By using the difference between synergy and redundancy, a few cases were found where the two predictors can be considered together for CIs’ estimation. Coherence analysis through the wavelet transform for three variables, both through multiple and partial analysis, provides the time intervals and bands of periods, where the two considered predictors can be used together or separately. The results differ depending on the predictand, the season and the considered lags. Significant information is brought out by using the two predictors together, namely the summer season, for GBOI and NAOI, when the predictors were taken 2 years before, and the winter season, as AMO responds to the variations of both solar and geomagnetic activity after 4 years

Discriminant Analysis of the Solar Input on the Danube’s Discharge in the Lower Basin

This paper presents the extent to which the combination of extra-atmospheric and hydroclimatic factors can be deciphered to record their contribution to the evolution and forecasting of the Danube discharge (Q) in the lower basin. A combination of methods such as wavelet filtering and deep learning (DL) constitutes the basic method for discriminating the external factors (solar activity through Wolf numbers) that significantly contribute to the evolution and prediction of the lower Danube discharge. An ensemble of some of the most important factors, namely, those representing the atmospheric components, i.e., the Greenland-Balkan Oscillation Index (GBOI) and the North Atlantic Oscillation Index (NAOI); the hydroclimatic indicator, the Palmer Hydrological Drought Index (PHDI); and the extra-atmospheric factor, constitutes the set of predictors by means of which the predictand, Q, in the summer season, is estimated. The external factor has to be discriminated in the Schwabe and Hale spectra to make its convolutional contribution to the Q estimation in the lower Danube basin. An interesting finding is that adding two solar predictors (associated with the Schwabe and Hale cycles) to the terrestrial ones give a better estimation of the Danube discharge in summer, compared to using only terrestrial predictors. Based on the Nash–Sutcliffe (NS) index, a measure of performance given by the extreme learning machine (ELM), it is shown that, in association with certain terrestrial predictors, the contribution of the Hale cycle is more significant than the contribution of the Schwabe cycle to the estimation of the Danube discharge in the lower basin