Correlation Between the 22-Year Solar Magnetic Cycle and the 22-Year Quasicycle in the Earth's Atmospheric Temperature

Qu, Weizheng; Zhao, Jingrui; Huang, Fei; Deng, Shenggui

doi:10.1088/0004-6256/144/1/6

Cited by 14 publications

(13 citation statements)

References 11 publications

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“…Such oscillations were recently discovered by Offermann et al (2015). An oscillation with a period of about 20 to 25 years is found in various atmospheric parameters such as temperature (Qu et al, 2012;Wei et al, 2015), geopotential height (Coughlin and Tung, 2004a, b), and planetary wave activity (Jarvis, 2006;Höppner and Bittner, 2007). It is also seen in two atmospheric models (HAMMONIA, WACCM).…”

Section: Long-term Oscillationmentioning

confidence: 82%

“…Several studies exist showing a quasi 22-year modulation of different meteorological parameters such as temperature, rainfall, and temperature variability that are in phase with the Hale cycle or the double sunspot cycle (e.g. Willet, 1974;King et al, 1974;King, 1975;Qu et al, 2012), but no physical mechanism is found for these coincidences. The double sunspot cycle is another type of Hale cycle with a period of about 22 years which is phase-shifted compared to the Hale cycle of the solar polar magnetic field.…”

mentioning

confidence: 99%

“…The maxima and minima of the double sunspot cycle occur at maxima of the sunspot number (e.g. King, 1975;Qu et al, 2012). However, a number of possible influences, also showing a 22-year modulation, are named: galactic cosmic rays (GCR), solar irradiation, and solar wind (e.g.…”

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confidence: 99%

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Long-term dynamics of OH * temperatures over central Europe: trends and solar correlations

et al. 2016

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Abstract. We present the analysis of annual average OH * temperatures in the mesopause region derived from measurements of the Ground-based Infrared P-branch Spectrometer (GRIPS) at Wuppertal (51 • N, 7 • E) in the time interval 1988 to 2015. The new study uses a temperature time series which is 7 years longer than that used for the latest analysis regarding the long-term dynamics. This additional observation time leads to a change in characterisation of the observed longterm dynamics.We perform a multiple linear regression using the solar radio flux F10.7 cm (11-year cycle of solar activity) and time to describe the temperature evolution. The analysis leads to a linear trend of (−0.089 ± 0.055) K year −1 and a sensitivity to the solar activity of (4.2 ± 0.9) K (100 SFU) −1 (r 2 of fit 0.6). However, one linear trend in combination with the 11-year solar cycle is not sufficient to explain all observed long-term dynamics. In fact, we find a clear trend break in the temperature time series in the middle of 2008. Before this break point there is an explicit negative linear trend of (−0.24 ± 0.07) K year −1 , and after 2008 the linear trend turns positive with a value of (0.64 ± 0.33) K year −1 . This apparent trend break can also be described using a long periodic oscillation. One possibility is to use the 22-year solar cycle that describes the reversal of the solar magnetic field (Hale cycle). A multiple linear regression using the solar radio flux and the solar polar magnetic field as parameters leads to the regression coefficients C solar = (5.0 ± 0.7) K (100 SFU) −1 and C hale = (1.8 ± 0.5) K (100 µT) −1 (r 2 = 0.71). The second way of describing the OH * temperature time series is to use the solar radio flux and an oscillation. A leastsquare fit leads to a sensitivity to the solar activity of (4.1 ± 0.8) K (100 SFU) −1 , a period P = (24.8 ± 3.3) years, and an amplitude C sin = (1.95 ± 0.44) K of the oscillation (r 2 = 0.78). The most important finding here is that using this description an additional linear trend is no longer needed. Moreover, with the knowledge of this 25-year oscillation the linear trends derived in this and in a former study of the Wuppertal data series can be reproduced by just fitting a line to the corresponding part (time interval) of the oscillation. This actually means that, depending on the analysed time interval, completely different linear trends with respect to magnitude and sign can be observed. This fact is of essential importance for any comparison between different observations and model simulations.

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Section: Long-term Oscillationmentioning

confidence: 82%

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confidence: 99%

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Long-term dynamics of OH * temperatures over central Europe: trends and solar correlations

et al. 2016

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“…The correlation coefficients between various climate indices and the IMFs with periodicities ranging from inter-annual to decadal time scales are summarized in Table 2. Higher correlation coefficients are obtained between quasi-biennial oscillation (QBO) (NCEP, 2015) and IMF6, East Asian winter monsoon index (EAWMI) (Jhun and Lee, 2004) and IMF6þIMF7, solar cycle F10.7 cm index (NRC, 2015) and IMF8, and magnetic cycle (Qu et al, 2012) and IMF9 of 7 Be. Time series of these four components are displayed vis-a-vis QBO, EAWMI, solar cycle index and magnetic cycle, respectively, in Fig.…”

Section: Resultsmentioning

confidence: 99%

“…The variation of 7 Be suggests a period around 25 y (instantaneous frequency between 0.035 and 0.04 y À1 ) which is longer than the length of the record (17 y). It is interesting to note a strong coherence between the 22 y solar magnetic cycle (Qu et al, 2012) and IMF9 of 7 Be (Fig. 6d).…”

Section: Decadal and Bi-decadal Variations E Footprints For Solar Modmentioning

confidence: 86%

Time series study of a 17-year record of 7Be and 210Pb fluxes in northern Taiwan using ensemble empirical mode decomposition

Lee

Huh

Lee

et al. 2015

Journal of Environmental Radioactivity

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Low Altitude Atmospheric Circulation Characteristics of Arctic Oscillation and Its Relation to Solar Activity

et al. 2014

Chinese J of Geophysics

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A comparative analysis is made to the monthly average geopotential height, geopotential height anomaly, and temperature anomaly of the Northern Hemisphere at the place with an air pressure of 1000 hPa during the positive and negative phases of arctic oscillation (AO). The result shows that the abnormal warming period in the Arctic region corresponds to the negative phase of AO, while the anomalous cold period corresponds to its positive phase. It means that the abnormal change in the Arctic region is an important factor deciding the anomalies of AO. The AO phenomena occurring in January have a clear quasi‐110‐year centurial cycle and quasi‐22‐year decadal cycle, which are closely related to solar activities. The specific expressions are as follows: (1) The AO phenomena occurring in January have a clear quasi‐110‐year centurial cycle closely related to solar activities. The variance contribution rate of this cycle for AO indexes reaches 44.4%, which is the most significant centurial change characteristic of the AO phenomena in January. (2) The spectral analysis results show that the AO indexes in January after filtering out the 110‐year centurial variation have a significant 22‐year cycle, and their variance contribution rates reach 18.5%, which is the second important characteristics of the interdecadal changes of AO indexes after the quasi‐110‐year centurial cycle. The comparative analysis results show that there is a close inverse relationship between the solar activities (especially the solar magnetic field index changes) and the changes in the 22‐year cycle of the AO in January, and that the two trends are basically opposite to each other. That is to say, in most cases after the solar magnetic index MI rises from the lowest value, the solar magnetic field turns from north to south, and the high‐energy particle flow entering the Earth's magnetosphere increases to heat the polar atmosphere, thus causing the AO to drop from the highest value. After the solar magnetic index MI drops from the highest value, the solar magnetic field turns from south to north, and the solar high‐energy particle flow passes through the top of the Earth's magnetosphere rather than entering it to heat the polar atmosphere. And thus the polar temperature drops, causing the AO to rise from the lowest value. In summary, the variance contribution rate of the changes in the Quasi‐110‐year centurial cycle and Quasi‐22‐year decadal cycle to the AO is up to 62.9%, indicating that solar activity is an important driving factor of the AO.

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Correlation Between the 22-Year Solar Magnetic Cycle and the 22-Year Quasicycle in the Earth's Atmospheric Temperature

Cited by 14 publications

References 11 publications

Long-term dynamics of OH * temperatures over central Europe: trends and solar correlations

Long-term dynamics of OH * temperatures over central Europe: trends and solar correlations

Time series study of a 17-year record of 7Be and 210Pb fluxes in northern Taiwan using ensemble empirical mode decomposition

Low Altitude Atmospheric Circulation Characteristics of Arctic Oscillation and Its Relation to Solar Activity

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