In fluence of solar wind energy flux on the interannual variability of ENSO in the subsequent year

1. Introduction

It is well recognized that variations in solar irradiance,especially on quasi-decadal time scales, exert substantial effects on tropospheric climate (Christoforou and Hameed 1997; Gray et al. 2010; Herschel 1801; Liu and Lu 2010). Strong connections between the 11-year solar cycle (e.g. solar radio flux at 10.7 cm (F10.7) or sunspot number (SSN)) and climatic variability in the troposphere–lower stratosphere have been well documented(Ineson et al. 2011; Labitzke and Van Loon 1988, 1997;Loon and Labitzke 1988). For instance, both observational and modelling results have documented the changes in regional and global pressure systems associated with the 11-year solar cycle, including the eastward (southward)migration of the Aleutian low (Hawaiian high) during minimum sunspots years (Christoforou and Hameed 1997),apparent positive pressure anomalies over the Gulf of Alaska in November–January of peak sunspots years(van Loon and Meehl 2008; Loon and Meehl 2014), and positive phases of the North Atlantic Oscillation in winters of maximum solar cycles (Kodera 2003; Thiéblemont et al. 2015).

Over the Pacific, one of the most dramatic features of sea level pressure (SLP) is the Southern Oscillation (SO)(Rasmusson and Carpenter 1982), together with El Niño/La Niña events, collectively known as El Niño–Southern Oscillation (ENSO) (Zebiak and Cane 1987). Previous studies have reported that ENSO is related to the internal cycle of feedback within the tropical Pacific ocean–atmosphere climatic systems (Chen, Chen, and Yu 2017; Chen et al. 2016;Chen and Zhou 2012; Graham and White 1988; McCreary Jr 1983; Nuzhdina 2002). Many recent studies have revealed that the extratropical forcing associated with large-scale atmospheric circulation plays very important roles in the formation of ENSO (Chen et al. 2013, 2015; Chen, Yu, and Chen 2014, 2015). Controversially, it has also been claimed that external forcing such as volcanic aerosols (Emile-Geay et al. 2008; Handler 1984) and Pacific bottom seismic events (Walker 1995) can explain the variability of ENSO.Additionally, the contribution of the 11-year solar cycle to the interdecadal variability of ENSO has been widely discussed (Kirov and Georgieva 2002; Marchitto et al. 2010;Troshichev et al. 2005). As the solar cycle cannot directly re flect the total energy contributing to Earth’s atmosphere and is dominated by quasi-decadal variability (Ammann et al. 2007; Scafetta and West 2006), the interannual relationship between ENSO and solar activity, as well as the related mechanisms, is far from clear.

Although the total solar energy penetrating Earth’s atmosphere is considerably smaller than the total solar irradiance, the interannual variability of the energy input from the solar wind is much larger (Troshichev et al. 2005).Therefore, it is very interesting to examine the interannual relationship between the total energy input from the solar wind into Earth’s magnetosphere (Ein) and ENSO, which has rarely been discussed before because of the big challenge in quantitatively estimating Ein (Akasofu 1981; Newell et al. 2008). Based on a totally new Ein index, which is quantitatively estimated via three-dimensional magnetohydrodynamics (Wang et al. 2014), we reveal a statistically significant interannual relationship between the annual mean Ein and the subsequent early-winter ENSO.

“你又错了，天下既大，气量就不能太小。”刘雁衡的口气是淡淡的，然而也是傲然的，“不管你所说的那两位是谁，至少要再加上一个。”

而对B班同学，请同学们观察下面这个图形，说说它有几条边，有几个角，并且测量一下边的长度，角的度数，说说这些边和角有什么大小关系。

2. Data and methods

2.1. Energy input from the solar wind into Earth’s magnetosphere

No potential conflict of interest was reported by the authors.

Linear regression of early-winter velocity potential at lag(+1 yr) onto Ein shows a zonally oriented dipole pattern with opposite sign at the near surface (850 hPa ≈ 1.4 km)and upper level (100 hPa ≈ 16 km) (Figure 4(a) and (b)). At 850 hPa, a statistically significant anomalous divergence center (approximately −4.0 × 105 m2 s−1) is strictly confined to Darwin Island, accompanied by two statistically significant positive centers located around Tahiti Island and the Northeast Pacific (35°N, 130°W) (Figure 4(b), shading). Similar results but with opposite sign are apparent in the upper troposphere. Note that the velocity potential anomaly in the upper troposphere is stronger, with a magnitude as high as 1.4 × 106 m2 s−1 (Figure 4(a), shading). Additionally, apparent significant anomalous divergence and convergence winds appear where negative and positive velocity potential anomaly centers are located(Figure 4(a) and (b), vectors). It should be noted that the significant anomalous divergence/convergence winds are observed in the locations used to identify the SO indices (Figure 4(b), blue boxes), con firming the significant in fluence of Ein on the formation of the SO. This is further supported by the cross section (averaged between 35°S and 35°N) of velocity potential, which indicates that the anomalies west of the date line are opposite to those in the east, and the sign also reverses at ～400 hPa or ～7 km(Figure 4(c)). The con figuration of anomalous divergence/convergence indicates a pronounced weakening of the Pacific Hadley–Walker circulation (Bjerknes 1966), which is an important dynamical contributor to the formation of the SO (Schwing, Murphree, and Green 2002). As a result,corresponding to an increasing of the total energy input from the solar wind penetrating Earth’s magnetosphere,the atmospheric circulation in the subsequent early winter is characterized by anomalous surface westerly winds(～0.5 m s−1) across the central and eastern tropical Pacific(Figure 5(f), vectors). Meanwhile, anomalous rising motion over the eastern Pacific, a returning flow (～0.5 m s−1) from east to west at the upper level of the troposphere, and anomalous sinking motion over the western Pacific are observed (data not shown). The change in atmospheric circulation, especially the surface anomalous westerly(～0.5 m s−1) in the central and eastern tropical Pacific, leads to an El Niño-like SST anomaly pattern (Figure 5(a), shading) (Li 1990). In contrast, the early-winter surface wind related to the preceding SSN barely shows any significant anomalies (Figure 5(b), vectors). Correspondingly, the correlation between SSN and SST is much weaker (Figure 5(b),shading). We speculate that the solar ultraviolet irradiance effect associated with Ein and the atmospheric internal variability (i.e. Brewer–Dobson circulation) might be the main mechanism of such a significant lag correlation.

This study, based on a new index estimated by three-dimensional magneto hydrodynamic simulations (Wang et al. 2014), reveals a new statistically significant interannual relationship between the annual-mean solar wind energy penetrating Earth’s magnetosphere and the subsequent early-winter ENSO. The annual accumulation of solar wind energy may explain more of the total interannual variance of ENSO compared to SSN/F10.7. Therefore, this study suggests that, even though it might be a big challenge, describing the processes of energy transmission,conversion and dissipation well in the solar wind–magnetosphere–ionosphere coupled system is essential to understand climate change and improve climate prediction.

2.2. Spatial data and other indices

To give more detail on the different in fluences between Ein and SSN/F10.7, we present in Figure 3 the regression of early-winter SLP upon the preceding annual Ein and SSN.At lag(+1 yr) of high total solar wind energy penetrating Earth’s magnetosphere, the early-winter SLP shows statistically significant anomalies over Asia, the eastern Indian Ocean, the western Pacific, the North and South Pacific,and the United States (Figure 3(a)). A statistically signi ficant high-pressure anomaly (0.3–1.4 hPa) extends from Asia southward to the Maritime Continent (between 40°S and 40°N), and a significant low-pressure anomaly (−1.2 to −0.2 hPa) is located in the subtropical North (20°–60°N)and South (60°–20°S) Pacific. The spatial distribution derived from HadSLP2r is similar to that from the NCEP/NCAR reanalysis (Figure 3(b)), indicating the robustness of the results. Such an anomalous pressure pattern, resembling the SO (Rasmusson and Carpenter 1982; Schwing,Murphree, and Green 2002), has rarely been detected before by crude composite differences between maximum and minimum solar phases. As illustrated by Figure 3(c)and (d), the SLPAs related to SSN are less significant and smaller in magnitude than those related to Ein.

第四，制定与完善国际贸易融资相关的法律法规。国内立法机关的应加强对国际贸易融资法律法规和政策措施的研究，找出我国与国际其他国家在贸易融资方面的差距，并分析问题产生的原因，提出解决问题的具体对策。需要强调的是，相关研究人员要与国际惯例接轨，制定实施性强的法律法规，探索出国际贸易融资未来发展的光明之路。

3. Results

3.1. Different variability of Ein from SSN and F10.7

Figure 1. Normalized time series of annual-mean (a) SSN, (b) F10.7, and (c) Ein during 1963–2012. Morlet wavelet analysis for the standardized time series of annual-mean (d) SSN, (e) F10.7, and (f) Ein. Notes: The dotted regions are statistically significant at the 95% con fidence level for a red-noise process. Cross-hatched regions on either end indicate the ‘cone of in fluence’, where edge effects become important.

Figure 2. Evolution of the lagged (SO lags solar activity) correlation coefficients between the annual-mean (a) SSN, (b) F10.7, and (c) Ein during 1963–2012 and the seasonal-mean SO indices during 1964–2013. Note: The dashed lines indicate statistically significant correlation coefficients at the 90% con fidence level.

Considering that many previous studies have addressed the connection between SSN or F10.7 and climate (Huo and Xiao 2016; Nuzhdina 2002; Troshichev et al. 2005;Xiao and Li 2016; Xiao et al. 2017), we first discuss the difference between the solar wind energy and SSN/F10.7.Figure 1(a)–(c) display the normalized time series of annual-mean SSN, F10.7 and Ein during 1963–2012. It is apparent that SSN and F10.7 are dominated by low-frequency variability, with alternate positive and negative phases (Figure 1(a) and (b)). Morlet wavelet analysis indicates that SSN and F10.7 show clear low-frequency oscillation, with a period of about 11 years (Figure 1(d) and(e)). Moreover, the wavelet power of SSN and F10.7 with periods below 8 years is not statistically significant. In contrast, the periodicity of Ein is not as stable as that of SSN and F10.7. It displays both interannual and decadal variability(Figure 1(c)), which is more apparent by inspecting Figure 1(f). The indication is that Ein is dominated by variability with 2–4-year and 8–11-year periodicity (Figure 1(f)). These results are also supported by spectral analysis (data not shown). Additionally, compared with the high correlation coefficient of 0.988 between SSN and F10.7, the correlation coefficients of 0.637/0.647 between Ein and SSN/F10.7 imply a notable difference between Ein and SSN/F10.7.

Figure 3. (a, b) Regression maps of NCEP1/Hadley SLP (contours; units: hPa) in early winter 1964–2013 upon the Ein during 1963–2012.Light and dark shading indicates statistical significance at the 90% and 95% con fidence level, respectively. (c, d) As in (a, b) but regressed upon SSN. Notes: The pentagrams indicate the position of the centers of SLP in the North Pacific (35°N, 130°W), Darwin and Australia (10°S, 130°E), the South Pacific (30°S,95°W), and Tahiti (18°S, 150°E); the same in subsequent figures.

3.2. Linkages between atmospheric circulation and Ein

To con firm a robust linkage between ENSO and Ein, we adopt three different SO indices (NOI, SIO1, and SOI2, as outlined in Section 2.2). The temporal evolution of the lagged correlations between the annual-mean Ein and the seasonal-mean SO indices indicates that significant correlations start to emerge in the following autumn,with the strongest correlation in the following early winter (October to December) (Figure 2(c)). In contrast,such a statistically significant interannual relationship is not observed between the 11-year solar cycle (i.e. SSN or F10.7) and the SO indices (Figures 2(a) and (b)). This implies that the annual accumulation of solar energy potentially contributes to the interannual variability of the SO in the following early winter.

Monthly mean atmospheric circulation data are obtained from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP/NCAR)reanalysis (Kalnay et al. 1996), with a horizontal resolution of 2.5° × 2.5°. To support the results derived from the NCEP/NCAR reanalysis, observational gridded monthly SLP data from the Met Office Hadley Center (HadISLP2r)are employed (Allan and Ansell 2006), with a horizontal resolution of 5° × 5°. The sea surface temperature (SST)data is also from the Met Office Hadley Centre (Rayner et al. 2003), with a horizontal resolution of 1° × 1°.

Figure 4. Regression maps of the divergence component of the wind (div; vectors; units: m s−1) and velocity potential (VP; shading;units: 105 m2 s−1) at (a) 100 hPa and (b) 850 Pa in early winter 1964–2013 upon the Ein during 1963–2012. (c) As in (a) but for a vertical–longitude cross section of VP averaged over (35°S–35°N). Notes: Values enclosed by contours in (a) and (b) and stippling in (c) indicate statistically significant VP anomalies at the 90% con fidence level. The black arrows in(c) are a schematic representation of the anomalous Walker circulation.

Figure 5. Correlation/regression of SST (shading)/1000-hPa wind (vectors; units: m s−1) in early winter 1964–2013 upon the (a) Ein/ (b)SSN during 1963–2012. Cross-hatched areas indicate statistically significant values at the 90% con fidence level. Note: Winds are only displayed if either component of the wind anomalies is statistically significant at the 90% con fidence level.

3.3. Anomalous atmospheric dynamical processes of ENSO associated with Ein

选取某区域的城市独立坐标系成果与1980西安坐标系成果为试验数据。由于神经网络需要通过大样本进行训练，才能得到可靠的结果。本算例选择均匀分布的624个点作为学习样本，将另外的87 个点作为神经网络模型的检测样本，通过建立神经网络模型获取这87个点的1980西安坐标系成果，再对比其实际成果，计算检核精度。

4. Discussion

Recent analyses of the relationship between solar activity and atmospheric processes conducted by comparing two multi-decadal ocean–atmosphere chemistry–climate simulations with and without solar forcing variability revealed a significant response of the boreal winter atmosphere at lag(+1 yr) to the 11-year solar cycle (i.e. F10.7)(Thiéblemont et al. 2015). Although they found statistically significant SLPAs over the Atlantic–Arctic regions, the signals in other regions (e.g. the North Pacific) have not drawn much attention. As the 11-year solar cycle is dominated by quasi-decadal variability and cannot directly re flect the total energy contributed to Earth’s atmosphere, the relationship between solar activity and the atmosphere at the interannual time scale remains unclear.

Given that the atmosphere plays an important role in transferring the solar signal to the ocean (Thiéblemont et al. 2015), we first use SO indices to investigate the relationship between Ein and ENSO. Three SO indices are calculated from the monthly SLP anomaly (SLPA) for the period January 1964 to December 2013, based on the method proposed by Schwing, Murphree, and Green (2002). The Northern Oscillation Index (NOI), which is roughly the North Pacific equivalent of the SO index, is de fined as the difference in the SLPA between the climatological mean position of the center of the North Pacific high (35°N,130°W) and Darwin (10°S, 130°E). One SO index (SIO1) is de fined as the difference in the SLPA between the climatological mean position of the center of the South Pacific high (30°S, 95°W) and Darwin (10°S, 130°E), and the other(SOI2) is de fined as the difference in the SLPA between Tahiti (18°S, 150°W) and Darwin (10°S, 130°E). SSN and F10.7 indices are obtained from National Centers for Environmental Information of the NOAA (https://www.ngdc.noaa.gov/stp/solar/solar-indices.html). All correlation and regression analyses are based on the detrended datasets.

Here, nSW and VSW are the solar wind number density (units:cm−3) and solar wind velocity (units: km s−1), respectively;BT is the transverse magnetic field magnitude (units: nT),and θ is the interplanetary magnetic field clock angle.Solar wind data is obtained from NASA OMNIweb (http://omniweb.gsfc.nasa.gov/). It has been suggested that Ein performs better than the empirical parameter used by Perreault and Akasofu (1978) in quantitatively estimating the energy input on the global scale (Wang et al. 2014).

Disclosure statement

A three-dimensional magnetohydrodynamic simulation is used to quantitatively estimate Ein (units: W), which is de fined as follows (Wang et al. 2014):

硝酸；硝酸(1+1)；氢氟酸；盐酸；盐硝混酸：盐酸、硝酸、水的体积比为4∶1∶5；酒石酸；硝酸-酒石酸混酸：称取80g酒石酸，溶于1000mL硝酸中；银粒：质量分数不小于99.99%；银标准溶液：2.00mg/mL，称取2.0000g银粒置于250mL烧杯中，加40mL硝酸(1+1)，盖上表面皿，低温加热至完全溶解，煮沸至无黄色烟气产生，赶尽氮的氧化物，取下冷却至室温，用水吹洗表面皿及烧杯壁，移入1000mL棕色容量瓶中，并以水稀释至刻度，混匀，避光保存；氯化钠标准滴定溶液：0.1000mol/L，称取5.8440g氯化钠(基准试剂)，用水溶解，稀释至1000mL，混匀。

Funding

This research was supported by the National Key R&D Program of China [grant number 2016YFA0600703], the National Natural Science Foundation of China [grant numbers 41421004,41505073, and 41605059], and the Young Talent Support Plan launched by the China Association for Science and Technology[grant number 2016QNRC001].

References

Akasofu, S.-I. 1981. “Energy Coupling between the Solar Wind and the Magnetosphere.” Space Science Reviews 28: 121–190.

Allan, R., and T. Ansell. 2006. “A New Globally Complete Monthly Historical Gridded Mean Sea Level Pressure Dataset(HadSLP2): 1850–2004.” Journal of Climate 19: 5816–5842.

Ammann, C. M., F. Joos, D. S. Schimel, B. L. Otto-Bliesner, and R. A. Tomas. 2007. “Solar In fluence on Climate during the past Millennium: Results from Transient Simulations with the NCAR Climate System Model.” Proceedings of the National Academy of Sciences of the United States of America 104: 3713–3718.

Bjerknes, J. 1966. “A Possible Response of the Atmospheric Hadley Circulation to Equatorial Anomalies of Ocean Temperature.” Tellus 18: 820–829.

Chen, S., W. Chen, and B. Yu. 2017. “The In fluence of Boreal Spring Arctic Oscillation on the Subsequent Winter ENSO in CMIP5 Models.” Climate Dynamics 48: 2949–2965.

Chen, S., W. Chen, B. Yu, and H. F. Graf. 2013. “Modulation of the Seasonal Footprinting Mechanism by the Boreal Spring Arctic Oscillation.” Geophysical Research Letters 40: 6384–6389.

Chen, S., R. Wu, W. Chen, and B. Yu. 2015. “In fluence of the November Arctic Oscillation on the Subsequent Tropical Pacific Sea Surface Temperature.” International Journal of Climatology 35: 4307–4317.

Chen, S., R. Wu, W. Chen, B. Yu, and X. Cao. 2016. “Genesis of Westerly Wind Bursts over the Equatorial Western Pacific during the Onset of the Strong 2015–2016 El Niño.”Atmospheric Science Letters 17: 384–391.

Chen, S., B. Yu, and W. Chen. 2014. “An Analysis on the Physical Process of the In fluence of AO on ENSO.” Climate Dynamics 42: 973–989.

Chen, S., B. Yu, and W. Chen. 2015. “An Interdecadal Change in the In fluence of the Spring Arctic Oscillation on the Subsequent ENSO around the Early 1970s.” Climate Dynamics 44: 1109–1126.

Chen, W., and Q. Zhou. 2012. “Modulation of the Arctic Oscillation and the East Asian Winter Climate Relationships by the 11-Year Solar Cycle.” Advances in Atmospheric Sciences 29: 217–226.

Christoforou, P., and S. Hameed. 1997. “Solar Cycle and the Pacific ‘Centers of Action’.” Geophysical Research Letters 24:293–296.

Emile-Geay, J., R. Seager, M. A. Cane, E. R. Cook, and G. H. Haug.2008. “Volcanoes and ENSO Over the Past Millennium.”Journal of Climate 21: 3134–3148.

Graham, N. E., and W. B. White. 1988. “The El Niño Cycle: A Natural Oscillator of the Pacific Ocean-Atmosphere System.”Science 240: 1293–1302.

Gray, L. J., J. Beer, M. Geller, J. D. Haigh, M. Lockwood, K. Matthes,U. Cubasch, et al. 2010. “Solar In fluences on Climate.” Reviews of Geophysics 48 (4): RG4001. doi:10.1029/2009RG000282.

Handler, P. 1984. “Possible Association of Stratospheric Aerosols and El Nino Type Events.” Geophysical Research Letters 11:1121–1124.

Herschel, W. 1801. “Observations Tending to Investigate the Nature of the Sun, in Order to Find the Causes or Symptoms of Its Variable Emission of Light and Heat; with Remarks on the Use That May Possibly Be Drawn from Solar Observations.”Philosophical Transactions of the Royal Society of London 91:265–318.

Huo, W.-J., and Z.-N. Xiao. 2016. “The Impact of Solar Activity on the 2015/16 El Niño Event.” Atmospheric and Oceanic Science Letters 9: 428–435.

Ineson, S., A. A. Scaife, J. R. Knight, J. C. Manners, N. J. Dunstone, L.J. Gray, and J. D. Haigh. 2011. “Solar Forcing of Winter Climate Variability in the Northern Hemisphere.” Nature Geoscience 4:753–757.

Labitzke, K., and H. Van Loon. 1997. “The Signal of the 11-Year Sunspot Cycle in the Upper Troposphere-Lower Stratosphere.”Space Science Reviews 80: 393–410.

Kalnay, E., M. Kanamitsu, R. Kistler, W. Collins, D. Deaven, L.Gandin, M. Iredell, et al. 1996. “The NCEP/NCAR 40-Year Reanalysis Project.” Bulletin of the American Meteorological Society 77: 437–471.

Kirov, B., and K. Georgieva. 2002. “Long-Term Variations and Interrelations of ENSO, NAO and Solar Activity.” Physics and Chemistry of the Earth, Parts a/B/C 27: 441–448.

Kodera, K. 2003. “Solar In fluence on the Spatial Structure of the NAO during the Winter 1900–1999.” Geophysical Research Letters 30 (4): 1175. doi:10.1029/2002GL016584.

Labitzke, K., and H. Van Loon. 1988. “Associations between the 11-Year Solar Cycle, the QBO and the Atmosphere. Part I: The Troposphere and Stratosphere in the Northern Hemisphere in Winter.” Journal of Atmospheric and Terrestrial Physics 50:197–206.

Li, C. Y. 1990. “Interaction between Anomalous Winter Monsoon in East Asia and El Nino Events.” Advances in Atmospheric Sciences 7: 36–46.

Liu, Y., and C. H. Lu. 2010. “The In fluence of the 11-Year Sunspot Cycle on the Atmospheric Circulation during Winter.” Chinese Journal of Geophysics 53: 354–364.

Loon, H. V., and K. Labitzke. 1988. “Association between the 11-Year Solar Cycle, the QBO, and the Atmosphere. Part II:Surface and 700 Mb in the Northern Hemisphere in Winter.”Journal of Climate 1: 905–920.

van Loon, H., and G. A. Meehl. 2008. “The Response in the Pacific to the Sun’s Decadal Peaks and Contrasts to Cold Events in the Southern Oscillation.” Journal of Atmospheric and Solar-Terrestrial Physics 70: 1046–1055.

Loon, H., and G. A. Meehl. 2014. “Interactions between Externally Forced Climate Signals from Sunspot Peaks and the Internally Generated Pacific Decadal and North Atlantic Oscillations.”Geophysical Research Letters 41: 161–166.

Marchitto, T. M., R. Muscheler, J. D. Ortiz, J. D. Carriquiry, and A.van Geen. 2010. “Dynamical Response of the Tropical Pacific Ocean to Solar Forcing during the Early Holocene.” Science 330: 1378–1381.

McCreary Jr, J. P. 1983. “A Model of Tropical Ocean-Atmosphere Interaction.” Monthly Weather Review 111: 370–387.

Newell, P. T., T. Sotirelis, K. Liou, and F. Rich. 2008. “Pairs of Solar Wind-Magnetosphere Coupling Functions:Combining a Merging Term with a Viscous Term Works Best.” Journal of Geophysical Research 113: A04218.doi:10.1029/2007JA012825.

Nuzhdina, M. 2002. “Connection between ENSO Phenomena and Solar and Geomagnetic Activity.” Natural Hazards and Earth System Science 2: 83–89.

Perreault, P., and S. I. Akasofu. 1978. “A Study of Geomagnetic Storms.” Geophysical Journal International 54 (3): 547–573.

Rasmusson, E. M., and T. H. Carpenter. 1982. “Variations in Tropical Sea Surface Temperature and Surface Wind Fields Associated with the Southern Oscillation/El Niño.” Monthly Weather Review 110: 354–384.

Rayner, N. A., D. E. Parker, E. B. Horton, C. K. Folland, L. V.Alexander, D. P. Rowell, E. C. Kent, and A. Kaplan. 2003. “Global Analyses of Sea Surface Temperature, Sea Ice, and Night Marine Air Temperature since the Late Nineteenth Century.”Journal of Geophysical Research Atmospheres 108: 1063–1082.

Scafetta, N., and B. West. 2006. “Phenomenological Solar Contribution to the 1900–2000 Global Surface Warming.”Geophysical Research Letters 33: L05708. doi:10.1029/2005 GL025539.

Schwing, F., T. Murphree, and P. Green. 2002. “The Northern Oscillation Index (NOI): A New Climate Index for the Northeast Pacific.” Progress in Oceanography 53: 115–139.

Thiéblemont, R., K. Matthes, N.-E. Omrani, K. Kodera, and F.Hansen. 2015. “Solar Forcing Synchronizes Decadal North Atlantic Climate Variability.” Nature Communications 6: 8268.

Troshichev, O., L. Egorova, A. Janzhura, and V. Vovk. 2005.“In fluence of the Disturbed Solar Wind on Atmospheric Processes in Antarctica and El-Nino Southern Oscillation(ENSO).” Memorie Societa Astronomica Italiana 76 (4): 890.

Walker, D. A. 1995. “More Evidence Indicates Link between El Niños and Seismicity.” EOS, Transactions American Geophysical Union 76: 33–36.

Wang, C., J. Han, H. Li, Z. Peng, and J. Richardson. 2014. “Solar Wind-Magnetosphere Energy Coupling Function Fitting:Results from a Global MHD Simulation.” Journal of Geophysical Research: Space Physics 119: 6199–6212.

Xiao, Z., and D. Li. 2016. “Solar Wind: A Possible Factor Driving the Interannual Sea Surface Temperature Tripolar Mode over North Atlantic.” Journal of Meteorological Research 30: 312–327.

Xiao, Z.-N., D.-L. Li, L.-M. Zhou, L. Zhao, and W.-J. Huo. 2017.“Interdisciplinary Studies of Solar Activity and Climate Change.” Atmospheric and Oceanic Science Letters 1–4.

Zebiak, S. E., and M. A. Cane. 1987. “A Model El Niño-Southern Oscillation.” Monthly Weather Review 115: 2262–2278.