Solar and Anthropogenic Influences on Climate: Regression Analysis and Tentative Predictions

The paper aims to quantify solar and anthropogenic influences on climate change, and to make some tentative predictions for the next hundred years. By means of double regression, we evaluate linear combinations of the logarithm of the carbon dioxide concentration and the geomagnetic aa index as a proxy for solar activity. Thereby, we reproduce the sea surface temperature (HadSST) since the middle of the 19th century with an adjusted R2 value of around 87 percent for a climate sensitivity (of TCR type) in the range of 0.6 K until 1.6 K per doubling of CO2. The solution of the double regression is quite sensitive: when including data from the last decade, the simultaneous occurrence of a strong El Niño and of low aa values leads to a preponderance of solutions with relatively high climate sensitivities around 1.6 K. If these later data are excluded, the regression delivers a significantly higher weight of the aa index and, correspondingly, a lower climate sensitivity going down to 0.6 K. The plausibility of such low values is discussed in view of recent experimental and satellite-borne measurements. We argue that a further decade of data collection will be needed to allow for a reliable distinction between low and high sensitivity values. In the second part, which builds on recent ideas about a quasi-deterministic planetary synchronization of the solar dynamo, we make a first attempt to predict the aa index and the resulting temperature anomaly for various typical CO2 scenarios. Even for the highest climate sensitivities, and an unabated linear CO2 increase, we predict only a mild additional temperature rise of around 1 K until the end of the century, while for the lower values an imminent temperature drop in the near future, followed by a rather flat temperature curve, is prognosticated.

Impacts of sunspot number and Geomagnetic aa-index on climate of Wet Zone West Africa during solar cycles 22–24

Using the facilities at Heliophysics Science Division of NASA Goddard Space Flight Center, Greenbelt, MD, USA, we attempted to investigate the impact of solar magnetic activities on the climate of Wet Zone West Africa. The solar activity data namely, Sunspot Number (SSN) was obtained from the Royal Observatory of Belgium, Brussels; and Geomagnetic aa-index was obtained from World Data Center, Kyoto, Japan. Surface Air Temperature (SAT) and Rainfall data [for Port Harcourt in Nigeria and Abidjan in Cote D’Ivoire] were obtained from the HadCRUT-4 project of Climate Research Unit of University of East Anglia, United Kingdom. Firstly, we carried out Time Series Analysis of SSN and Geomagnetic aa-index spanning from 1950 to 2016. Secondly, we performed Regression Analysis on both solar activity data and climate variables to estimate the impact of solar magnetic activity on the Wet Zone West African climate. The Time Series Analysis showed that SSN variation was in-phase with Geomagnetic aa-index in all the solar cycles studied. Thus, Geomagnetic aa-index can be used as a proxy for studying solar magnetic activities. Performance of Regression Analysis showed that SSN regressed on SAT and Rainfall amounted to an average of 0.49 and 0.02% respectively throughout Solar Cycles 22–24. Furthermore, a regression of Geomagnetic aa-index on SAT and Rainfall yielded an average of 0.145 and 0.125% respectively. Our models showed that the variability of SAT and Rainfall in Wet Zone West Africa during Solar Cycles 22–24 are far less than 1%. Hence, the influence of SSN and Geomagnetic aa-index on SAT and Rainfall is less than 1%; and could cause ‘very small’ effect. These weak impacts are proofs that the variability of SAT and Rainfall were most probably not effected by SSN and Geomagnetic aa-index. Consequently, the variability of SAT and Rainfall in Wet Zone West Africa could not be attributed to SSN and Geomagnetic aa-index. We therefore, attempt to conclude that climate variability in Wet Zone West Africa is most probably not driven by solar magnetic activity, but could be attributed to anthropogenic activities.

Separating the aa-index into Solar and Hale Cycle Related Components Using Principal Component Analysis

We decompose the monthly aa-index of Cycles 10 to 23 using principal component analysis (PCA). We show that the first component (PC1) is related to the 11-year solar cycle, and accounts for 41.5% of the variance of the data. The second component (PC2) is related to 22-year Hale cycle, and explains 23.6% of the variance of the data. The PC1 time series of the aa-index for Cycles 10 – 23 has only one peak in its power spectrum at the period 10.95 years, which is the average solar cycle (SC) period for the interval SC10 – SC23. The PC2 time series of the same cycles has a clear peak at period 21.90 (Hale cycle) and a smaller peak at 3/4 of that period. We also study the principal components of the sunspot number (SSN) for Cycles 10 – 23, and compare the mutual behavior of the PC2 components of the aa-index and SSN PCA analyses. We note that they are in the same phase in all other cycles but Solar Cycles 15 and 20. The aa-index of Cycle 20 also differs from other even aa-index cycles in its shape, especially in anomalously high peaks during its descending phase. Even though there is a coherence in the PC2 time series phases of the aa-index and sunspot number, this effect is too small to be the origin of all the differences between the shape of even and odd aa cycles. We estimate that 30% of the shape of the PC2 component of the aa-index is due to the shape of the PC2 of the sunspot number and the rest to other recurrent events in the Sun and solar wind. The first maximum of the aa-index (typical to odd cycles), during sunspot maximum, has been shown to be related to coronal mass ejections (CME), while the second maximum (typical to even cycles) in the cycle descending phase, is probably related to high-speed streams (HSS). The last events increase the activity level such that the minimum between even and odd cycle pairs is always higher than the minimum between succeeding odd and even cycle pairs.

A clock for the Sun's magnetic Hale cycle and 27 day recurrences in the aa geomagnetic index

<p>We construct a new solar cycle phase clock which maps each of the last 18 solar cycles onto a single normalized epoch for the approximately 22 year Hale (magnetic polarity) cycle, using the Hilbert transform of daily sunspot numbers (SSN) since 1818. We use the clock to study solar and geomagnetic climatology as seen in datasets available over multiple solar cycles. The occurrence of solar maxima on the clock shows almost no Hale cycle dependence, confirming that the clock is synchronized with polarity reversals.  The odd cycle minima lead the even cycle minima by ~ 1.1 normalized years, whereas the odd cycle terminators (when sunspot bands from opposite hemispheres have moved to the equator and coincide, thus terminating the cycle, McIntosh(2019)) lag the even cycle terminators  by ~ 2.3 normalized years.  The average interval between each minimum and terminator  is thus relatively extended for odd cycles and shortened for even ones. We re-engineer the R27 index that was orignally proposed by Sargent(1985) to parameterize 27 day recurrences in the aa index. We perform an epoch analysis of autocovariance in the aa index using the Hale cycle clock to obtain a high time resolution parameter for 27 day recurrence, <acv(27)>. This reveals that the transition to recurrence, that is, to an ordered solar wind dominated by high speed streams, is fast, occurring within 2-3 solar rotations or less. It resolves an extended late declining phase which is approximately twice as long on even Schwabe cycles as odd ones. We find that Galactic Cosmic Ray flux rises in step with <acv(27)> but then stays high. Our analysis also identifies a slow timescale trend in SSN that simply tracks the Gleissberg cycle. We find that this trend is in phase with the slow timescale trend in the modulus of sunspot latitudes, and in antiphase with that of the R27 index.</p>

On the relationship between the aa index and tidal forces

<abstract><p>A previously unknown relationship between the tidal forces and aa index is shown and used to discuss when the next maximum of the annual aa index is expected to occur.</p></abstract>

Estimating the maximum of the smoothed highest 3-hourly <i>a</i><i>a</i> index in 3 d by the preceding minimum for the solar cycle

Predicting the maximum intensity of geomagnetic activity for an upcoming solar cycle is important in space weather service and for planning future space missions. This study analyzed the highest and lowest 3-hourly aa index (aaH∕aaL) in a 3 d interval, smoothed by 363 d to analyze their variation with the 11-year solar cycle. It is found that the maximum of aaH (aaHmax) is well correlated with the preceding minimum of either aaH (aaHmin, r=0.85) or aaL (aaLmin, r=0.89) for the solar cycle. Based on these relationships, the intensity of aaHmax for solar cycle 25 is estimated to be aaHmax(25)=83.7±6.9 (nT), about 29 % stronger than that of solar cycle 24. This value is equivalent to the ap index of apmax(25)=47.4±4.4 (nT) if employing the high correlation between ap and aa (r=0.93). The maximum of aaL (aaLmax) is also well correlated with the preceding aaHmin (r=0.80). The maximum amplitude of the sunspot cycle (Rm) is much better correlated with high geomagnetic activity (aaHmax, r=0.79) than with low geomagnetic activity (aaLmax, r=0.37). The rise time from aaHmin to aaHmax is weakly anti-correlated to the following aaHmax (r=-0.42). Similar correlations are also found for the 13-month smoothed monthly mean aa index. These results are expected to be useful in understanding the geomagnetic activity intensity of solar cycle 25.

Predicting the maximum <i>aa</i>/<i>Ap</i> index through its relationship with the preceding minimum

Predicting the strength and peak time of geomagnetic activity for the ensuing cycle 25 is important in space weather service for planning future space missions. The minimum aa geomagnetic index around the solar minimum has been often used to predict the maximum amplitude of sunspot cycle, but seldom used to directly predict the maximum aa index. This study analyzed the relationships between the maxima and minima of both the geomagnetic aa and Ap indices for the 11-year cycle. The maximum aa index is found to be well correlated to the preceding minimum with a correlation coefficient of r = 0.860. As a result, the maximum aa index for the ensuing cycle 25 is predicted to be aamax(25) = 26.9 ± 2.6. This value is equivalent to Apmax(25) = 17.3 ± 1.8 ± 1.2 if employing the high correlation between aa and Ap (r = 0.939). The maximum Ap index is also found to be well correlated to the preceding minimum with a correlation coefficient of r = 0.862. Based on this correlation, the maximum Ap index is predicted to be a slightly higher value of Apmax(25) = 19.0 ± 1.6. The rise time of the aa (Ap) index for the 11-year cycle is found to be nearly uncorrelated to the following maximum, r = −0.16 (−0.17). If the data point for cycle 24 (which is far from others) were not considered, the rise time of the Ap index for the 11-year cycle would be weakly correlated to the following maximum, r = −0.404 at a confidence level of 62 %. The rise time for cycle 25 would be roughly estimated to be 89.9 ± 31.6 (months), implying that the geomagnetic activity for the ensuing cycle 25 would peak around April 2025 ± 32 months.