Delineation of possible influence of solar variability and galactic cosmic rays on terrestrial climate parameters

Journal Pre-proofs Delineation of possible influence of solar variability and galactic cosmic rays on terrestrial climate parameters A.K. Singh, Asheesh Bhargawa PII: DOI: Reference:

S0273-1177(20)30024-7 https://doi.org/10.1016/j.asr.2020.01.006 JASR 14603

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Advances in Space Research

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17 July 2019 7 January 2020 9 January 2020

Please cite this article as: Singh, A.K., Bhargawa, A., Delineation of possible influence of solar variability and galactic cosmic rays on terrestrial climate parameters, Advances in Space Research (2020), doi: https://doi.org/ 10.1016/j.asr.2020.01.006

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Delineation of possible influence of solar variability and galactic cosmic rays on terrestrial climate parameters A. K. Singh* and Asheesh Bhargawa Physics Department, University of Lucknow, Lucknow-226 007 Abstract–The present paper has investigated the associations of solar activity (SA), represented by total solar irradiance (TSI), galactic cosmic rays (GCR) andterrestrial climate parameters in particular the global cloudiness and global surface temperature. To that end, we have analysed thirty five years (1983 - 2018) data of these parameters and have applied the Granger-causality test in order to assess whether there is any potential predictability power of one indicator to the other. The correlations among the involved parameters are tested using Vector Auto Regression (VAR) model and variance decomposition method. As a result of the above analysis, we have found that the TSI is an important factor and has contributed about 8.77±0.42% in the cosmic ray intensity variations. In case of cloud cover variations, the other three parameters (TSI, cosmic ray and global surface temperature) have played a significant role. Further, the TSI changes have contributed1.68±0.03% fluctuations in the variance of the cloud cover while the cosmic ray intensity and global surface temperature have contributed about 4.89±0.08% and 10.87±1.41% respectively. In case of the global surface temperature anomaly both TSI and cloud covers have contributed about 5.07±0.47% and 14.42±2.13% fluctuations respectively. Additionally, we have also assessed the impact of internal climate oscillations like multivariate ENSO index (MEI), north Atlantic oscillations (NAO) and quasi biennial oscillations (QBO) on cloud cover variations. The contribution of these internal oscillations e.g. ENSO, NAO and QBO in cloud cover variation were reported as 7.48±1.02%, 5.51±0.16% and 1.36±0.43% respectively. Key words:

Granger causality test; cosmic ray intensity; Total solar irradiance (TSI); cloud cover; global surface temperature.

# *Corresponding Author:

Prof. Ashok Kumar Singh Department of Physics University of Lucknow Lucknow-226 007, India E-mail: [email protected] Mobile: +91-9415371523

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1. Introduction Cosmic rays are high energy (>GeV) subatomic particles consisting of ~85% protons (hydrogen nuclei), 12% α- particles (helium nuclei), 1% heavier nuclei and ~2% electrons are continuously reaching towards the lower atmosphere as radiation and impacting the terrestrial atmosphere in various ways (Kudela, 2009; Mewaldt, 2010; Singh et al., 2011).Emanated from the Sun or from outside of solar system within the Milky Way galaxy or from the inter galactic space, these high- energetic particles also contribute to the state of Earth’s atmosphere including, weather, and climate and human activities (Singh et al., 2010; Singh et al., 2011; Singh and Tonk, 2014). Variations in cloud cover can strongly change the fluxes of incoming (shortwave solar radiation) and outgoing (long wave) radiations of the Earth’s atmosphere and, thus, affects heavily to the heat balance of the atmosphere. The high/ low level clouds contribute to warming/cooling of the atmosphere and a net influx of radiation coming to the Earth’s surface during cloudy conditions depends on latitude, season and underlying surface conditions (Veretenenko et al., 2018). Three major mechanisms have been considered to link the solar variability and the　Earth’s climatic conditions. The first mechanism is based on total solar irradiance (TSI) variations that provide a changeable heat input to the lower atmosphere (Herschel, 1801). Various workers have shown additional correlations among solar and other geophysical parameters since then (Hoyt and Schatten, 1997; 　 Kopp and Lean, 2011). This indicates possible solar influences on meteorological and climate parameters such as temperature, thunderstorm frequency, tropopause heights, atmospheric circulation, and occurrences of droughts etc. at various time scales. Fröhlich and Lean (1998) have established that variation in solar irradiance is ~0.1% on decadal time scales which is responsible for the change in solar induced global average temperature by 0.1K (Wigley and Raper, 1990). Solar ultraviolet radiation is the second suggested forcing mechanism which changes substantially during a solar cycle and it is based on the variation of ozone

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concentrations and heating of the stratosphere with absorbance of the ultraviolet radiation (Haigh, 1996; Bhargawa et al., 2019). The third suggested mechanism is connected with the possible influence of galactic cosmic rays on the weather and climate (Wilcox, 1975), cloud processes (Dickinson, 1975), thunderstorm electrification (Markson and Muir, 1980), or ice formation in cyclones (Tinsley and Den, 1991). In the present article we have restricted ourselves to this mechanism. The cosmic rays undergo hadronic interactions impacting upon the atmosphere and resulting particles either decay or further interact and generate other particles. All charged particles produced in the process undergo electromagnetic interactions and thus form the ‘electromagnetic component’ of an air shower into the atmosphere. If the energy of the particle is sufficient in this process, electrons will be knocked down and ionized while traversing through the atmosphere. The primary energy will also decide the location of the peak of atmospheric ionization (Atri et al., 2010). The quantitative mechanism of cosmic ray induced atmospheric ionization’s role in producing thunderstorms is not established yet, however widely accepted mechanism is connected to electrons generated by the air showers. These energetic electrons can knock down more and more electrons, resulting in more ionization due to the avalanche of the electrons. At the value of critical energy, electrons begin to be relativistic runways and result into an abrupt discharge and energy released in the process produces thunderstorms (Gurevich and Zybin, 2001; Gurevich et al., 2001). Galactic cosmic rays are also known to modulate the global electrical circuit. Galactic cosmic rays directly change the concentration of atmospheric ions and affects the charges in the troposphere though the modulation of current flow in the global electric circuit indirectly (Tinsley, 2008).Based on some experiments, Alexeenko et al. (2002) have observed a strong correlation between the cosmic ray intensity and the magnitude of electric field disturbances and have showed different kind of cloud layers produce different types of thunderstorms that vary in their magnitude. The question whether cosmic rays affect the cloud

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cover or they have their impact on the climate (Svensmark, 1998) has been a topic of intense debate (Erlykin et al., 2011). Svensmark, (1998) argued that an increment in comic ray intensity will be responsible for increased rate of ionization in the atmosphere and further it will result in the increased cloud formation rate. So as a result of increased cloud cover less amount of radiation will reach to the Earth’s surface and will response for global cooling (Marsh and Svensmark, 2000a). Kirkby et al. (2011) have established that low level clouds have a net cooling effect, because they have a high albedo, and, being nearly as warm as the surface, they emit nearly as much infrared radiation to space as would the surface under clear skies. There were several studies which claimed correlation between GCR and amount as well as properties of low clouds but this correlation is not seen in case of middle and high clouds (Svensmark and Friis-Christensen, 1997; Marsh and Svensmark, 2000b; Harrison, 2008). Svensmark and Friis-Christensen (1997) have obtained a noticeable result showing the cloudGCR correlation on the decadal time scale. They have noticed a variation in global cloud amount (about 3-4%) showing a strong correlation with GCR intensity for the limited period of time (1983-1994).This correlation between GCR and properties of low clouds indicated the possible impact of solar activity on low clouds and ultimately on climate. The correlation of low-cloud factor and cosmic-ray flux is unexpected as the maximum degree of ionization by cosmic rays occurs in the altitude range 12–15 km, i.e. close to or above the tropopause. Thus any cosmic-ray induced cloud effect would be expected to be stronger for high rather than low-cloud layers (Jorgensen and Hansen 2000; Laken et al, 2012).The main difference between high and low clouds is that the high clouds are colder. An explanation may lie in the fact that, the physical state of the cloud droplets may play a significant role in the cosmic-ray–cloud interaction (Bondo et al., 2010). It has been pointed out before that the physics of ice and liquid clouds may differ (Harrison, 2008). By analysing different low-cloud types separately we found that clouds in a liquid phase account for almost all the variability during the observed period, leaving the ice

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clouds constant in time, except at the poles where a slight increasing trend for some of the ice cloud types is found. Thus the greater sensitivity of low cloud to cosmic rays may result from the preponderance of liquid-phase cloud types at lower altitudes (less than 6–7 km). With the result of stochastic changes in the solar magnetic activity and changes in the geomagnetic field on centennial and millennial time scales, cosmic ray intensity has varied accordingly (Usoskin, 2013). Beer et al. (1990) had reported about 15% decline in cosmic ray intensity reconstructed from 10Be concentrations in ice cores during the 20th century on account of an increase in the solar open magnetic flux by more than a factor of 2. Norris et al., (2016) have studied several independent, empirically corrected satellite records exhibiting large-scale patterns of cloud change between the 1980s and the 2000s. They have shown a consistency between the observed and simulated cloud change patterns. These results indicate that the cloud changes most consistently predicted by global climate models are currently occurring in nature. Pierce and Adams, (2009) have presented the calculation of the magnitude of the ion‐aerosol clear‐air mechanism using a general circulation model. With their simulations they have concluded that changes in CCN from changes in cosmic rays during a solar cycle are too small to play a significant role in current climate change. Several recent studies confirmed these results based on the new experiments and model simulations (Chiodo et al, 2014; Dunne et al., 2016; Pierce, 2017). Determining the true causality not only requires the establishment of a relationship between the two variables but also the far more difficult task of determining a direction of the causality. Although they do not provide information regarding directionality, correlation-based methods such as the lagged linear regression remain popular and useful tools for identifying lagged relationships between climate variables (McCracken et al., 2004). So the more data oriented and simulation approach can be beneficial in assessing cause-effect relationships between external forcing and temperature behaviour of various climate parameters. The Granger

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causality test has been used widely to check the causality between the two time series. It has already been used to analyse the results of an ocean drilling program, causal influences of the snow cover and vegetation on temperatures in different seasons (Diks and Mudelsee, 2000; Kaufmann et al., 2003, 2007) as well to analyse time series of the global and sea surface temperatures (Elsner, 2006, 2007) and the relationship between El Niño Southern Oscillation (ENSO) to the strength of Indian monsoons (Mohkov et al., 2011). In the present study, we have investigated the possible causal relations between solar activity and cloud cover as well as global temperature changes by applying the Granger causality test (Granger, 1969). Further, the variance decomposition method has been applied to calculate percentage contribution of one variable in the fluctuation of other. The paper is organised in few sections. Section 1 is dealing with the introductory remarks. The methodology and data analysis are presented in Section 2. Section 3 has described the association of cosmic rays with some climate parameters while Section 4 has presented the results and discussion. Conclusions are presented in Section 5. 2. Data Analysis and Methodology For present work, we have analysed 35 years (1983-2018) data of four solar/climate parameters namely total solar irradiance (TSI), comic ray intensity, cloud cover anomaly and global surface temperature. The TSI data was taken from the LASP Interactive Solar Irradiance Data Centre available at http://lasp.colorado.edu/lisird/data/sorce_tsi/. Cosmic rays data was obtained from the website of Oulu University (https://cosmicrays.oulu.fi/), the cloud cover data was

taken

from

the

International

Satellite

Cloud

Climatology

Project

(ISCCP)

(http://isccp.giss.nasa.gov/) while the global surface temperature anomaly data was obtained from

the

Earth

Sciences

Division,

Goddard

Institute

for

Space

Studies

(GISS),

NASA (https://data.giss.nasa.gov/gistemp/).The data for multivariate ENSO index (MEI), north

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Atlantic oscillations (NAO) and quasi biennial oscillations (QBO) was taken from website of NOAA (https://www.esrl.noaa.gov/psd/data/climateindices/index.html). We have used the International Satellite Cloud Climatology Project (ISCCP) data for cloud cover which is obtained from a fleet of geostationary satellites where cloud quantities are derived from infrared or visible measurements. As ISCCP offers several different cloud cover datasets, we have chosen ISCCP H series monthly mean data and employed IR low cloud amount (%)over the tropical and subtropical region (400 N to 400 S) for this study. 2.1. Quality and reliability of cloud data Since July 1983 ISCCP has collected, normalized, and calibrated radiance data (visible and thermal infrared) from the imaging radiometers on board the NOAA polar satellites and from the GOES, Meteosat, and GMS geostationary satellites. Examination of the 8 year cloud climatology produced with the first version of the ISCCP calibration revealed artifacts in the global means that coincided with the changes in the afternoon polar orbiters (used as a reference standard), as well as some localized anomalies related to occasional errors in the geostationary normalizations (Brest et. al., 1997). After this the changes to the ISCCP normalization and calibration procedures have been made to reduce these artifacts and errors and to produce a revised calibration. Several studies including Stubenrauch (2013) showed that long-term analyses may be problematic at best by using these cloud data due to the known errors and not very accurate calibrations. ISCCP was started in the year 1982 as a project to develop the understating related to climatology of cloud radiative properties. The legacy dataset known as ISCCP D series ended in 2009. The new processing is now available as the ISCCP H series. There were many improvements made in this new ISCCP H series (Tzallas et al., 2019). These improvements include: ISCCP now uses input data from full-resolution AVHRR Global Area Coverage (GAC) and ~10 km geostationary imagery—an increase from ~30 km in the ISCCP D-series (Higher

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Resolution Input Satellite Data). The higher resolution inputs allow 1 degree gridded products (Higher Resolution Gridded Products). The H series expands the period of record to 1982–2018 and will continue to produce updates (Expanded Period of Record). Neural network profiles from the HIRS provide stable retrievals during the period of record (Temporally Stable Atmospheric Profiles). Radiances and cloud information are available at pixel-level - 10 km - globally every 3 hours (Tzallas et al., 2019; Young et al., 2019). The International Satellite Cloud Climatology Project cloud analysis has small view angle dependence in its cloud estimate. There is about 10% more cloudiness reported at 60 degree view angle compared to normal views of the same scene (Campbell, 2004). Over the twenty year ISCCP record, more geosynchronous satellites have been added to the analysis and the mean view angle over the globe has become more vertical. This systematic change in view angle produced much of the decreasing trend in ISCCP cloud amount, both regionally and globally. This downward trend has recently been used to suggest widespread increases in surface solar heating, decreases in planetary albedo, and deficiencies in global climate models. Evan et al. (2007) showed that trends observed in the ISCCP data were satellite viewing geometry artifacts and were not related to physical changes in the atmosphere. Their results suggested that in its current form, the ISCCP data may not be appropriate for certain long-term global studies, especially those focused on trends. Clouds have a large impact on the Earth's radiation budget and hence have the potential to exert strong feedbacks on climate variability and climate change. These feedbacks are not well-understood, so it is essential to investigate observed relationships between cloud properties and other parameters of the climate system. Norris (2000, 2005) has described suitable cloud datasets based on surface observations and satellite observations and discussed various advantages and disadvantages of each data set. 2.2 Methods used

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We have applied the Granger causality test and the variance decomposition method on various parameters using Vector Auto Regression (VAR) model to identify the causal relationships among the considered parameters. This model was applied for univariate auto regression models to the multivariate case and they explain and/or predict the values of a set of variables at any given point of time (Sims, 1980). The basic p-lag vector auto-regression model has the form 𝑌𝑡 = 𝑐 + 𝛤1𝑌𝑡 - 1 + 𝛤2𝑌𝑡 - 2…… + 𝛤𝑝𝑌𝑡 - 𝑝 + 𝜀𝑡𝑡 = 1,2,3,……𝑇 where𝑌𝑡 = (𝑦1𝑡,𝑦2𝑡……,𝑦𝑛𝑡) has denoted as (n × 1) vector of time series variables, 𝛤𝑖are (n × n) coefficient matrices and 𝜀𝑡is an (n × 1) unobservable zero mean white noise vector process (serially uncorrelated or independent) with time invariant covariance matrix Σ. For example, a bivariate VAR model is defined by

( ) () (

)( ) (

)( ) ( )

𝑦1𝑡 𝑐1 𝛤111 𝛤112 𝑦1𝑡 - 1 𝛤211 𝛤212 𝑦1𝑡 - 1 𝜀1𝑡 = + + + 𝜀 1 1 2 2 𝑦2𝑡 𝑐2 𝑦 𝑦 𝛤21 𝛤22 2𝑡 - 1 𝛤21 𝛤22 2𝑡 - 1 2𝑡

Similarly, in lag operator notation, we can write the VAR (p). All variables are served as endogenous variables; each equation has the same exogenous variables and the lagged exogenous variables. This methodology was already used in various studies like hydrological indicators (Troin, 2018), structural uncertainty in climate scenarios (Gauthier et al., 2016). 3. Parametric correlations 3.1 Solar activity, total solar irradiance and cosmic rays Solar activity is related to the changes in the solar magnetic fields resulting in the phenomena like sunspots and solar flares. The combinations of radiation and magnetic activities play a major role in the cycle of solar activity. Changes in solar activity cycle and changes on the surface of the Sun such as sunspots darkening or faculae bright are responsible for the variation of total solar irradiance (Sinambela, 1998). Since the amount of solar energy emitted from the entire surface of the Sun varies periodically so the total solar irradiance will decrease with an

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increase in the sunspot area and will increase with the presence of the faculae area on the solar disc (Hoyt and Kyle, 1990). The 11-year solar activity cycle, the observational manifest of the solar dynamo, is apparent in indices such as the sunspot area and number, 10.7 cm radio flux and total solar irradiance (Sinambela, 1998; Butler et al., 2008). The measurements from the TSI radiometers onboard various satellites sent into space since 1978, collectively representing a nearly uninterrupted record, show clear solar cycle modulation (Ball et al., 2012). Total solar irradiance is defined as the total amount of solar radiation of all wavelengths, which falls on a unit area of the surface normal to the sun - earth line on a clear sky condition (Cossette et al., 2013).The average value of TSI ranges from 1364.61 to 1371.67 Wm-2. Variation in solar magnetic fields changes the flux of cosmic rays because the charged particles are modulated by the solar and geomagnetic fields (Morrill et al., 2011).During periods of high solar activity, less galactic cosmic rays (GCRs) are detected by neutron monitor son Earth. The total solar irradiance and cosmic rays show the anti-correlation, i.e., when total solar irradiance increases the cosmic rays actually decreases(Svensmark, 1998).We have noticed about 12.87±1.24% increase in the level of cosmic ray in comparison since 1990 (starting of solar cycle 22) to the present level (solar cycle 24) as indicated in Figure 1.Recently, Singh and Bhargawa (2017;2019) have predicted another solar minimum in the future and the cosmic rays at the Earth will further increase. The total solar irradiance (TSI) is 108 times larger in magnitude of energy flux than the cosmic rays. In most cases, the maximum energy of the solar particles is of the order of MeV but in case of solar flares or Coronal Mass Ejections (CMEs) it can reach up to the order of GeV (Reames, 1999; Erlykin and Wolfendale, 2010; Melott and Thomas, 2012). 3.2 Cosmic rays and cloud cover Studying the influence of solar activity and related phenomena on the lower atmosphere state, weather and climate is one of the important tasks of solar-terrestrial physics. One of the

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possible mechanisms of this influence suggests an impact of galactic cosmic rays (GCRs) on the cloud cover allowing amplifying noticeably a weak signal of solar variability in the Earth’s atmosphere (Svensmark and Friis-Christensen, 1997).Such variations in cloud cover can be related to variations in cosmic ray induced ionization in the atmosphere that decreases with the altitude with the maximum ionization production above the tropopause (12-15 km) (Dickinson, 1975). Several scientists criticized the reliability of Svensmark and Friis-Christensen (1997) claims and findings due to unsuitable data handling and statistical analyses (Laut, 2003; Gierens and Ponater, 1999; Jorgensen and Hansen, 2000). There was also concern by various researchers that the found correlation may be caused by other physical phenomena such as volcanic activity or El Niño with decadal periods (Cess et al., 2001; Wagner et al., 2005). Nevertheless, the observation has raised the intriguing possibility that Earth's climate could be affected by changes in cloudiness caused by variations in the intensity of galactic cosmic rays in the atmosphere (Marsh and Svensmark, 2000b; Carslaw et al., 2002). The proposed mechanism involves the cosmic ray induced atmospheric ionization where presence of ions enhances the growth of molecular clusters that in turn may grow further to aerosol particles with sufficient sizes to act as a cloud condensation nuclei(CCN). Since the water vapour super saturation in the atmosphere is too low to nucleate droplets from clean air the CCN particles with sufficient size are needed to form the droplets (Dickinson, 1975; Merikanto et al., 2009). Further, the indirect mechanisms for the ion contribution to the cloud formation are proposed in a process named ion induced nucleation (Yu and Turco, 2000; 2001). An important source of new aerosol particles in the atmosphere is the nucleation of ultrafine condensation nuclei from trace condensable vapours such as sulphuric acid (Zhang, 2010). The process train is supported by the presence of charge through the Coulomb attraction. Besides enhancing nucleation, charged aerosol particles resulting from cosmic ray ionization can also grow more rapidly than uncharged particles owing to the enhanced condensation rate. Carslaw et al. (2002)

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have explained and stated that the phase labelled coagulation and scavenging involves both the charge induced accretion and loss processes due to Coulomb interactions. However, Pierce and Adams (2009) found with their model simulations that despite of faster growth of ultrafine particles due to ionization, these particles then grow much slower later where only a fraction reaches the CCN sizes. Hypothesis of cosmic ray - cloud connection is still debated in the science, although in the last few years there is a strong scientific consensus that influence of cosmic rays on clouds is probably very small, localized or negligible. Here probably the largest progress in the field was done with the CERN CLOUD experiments, showing that organic vapours in the atmosphere can alone drive the nucleation and that the ion-induced aerosol nucleation has only minor contribution in forming the new CCN (Kirkby et al., 2011; Almeida et al., 2013; Kirkby et al., 2016). Detailed model studies (using GCMs) also confirmed that ion-induced aerosol nucleation has a minor part in CCN nucleation in the atmosphere (Pierce and Adams, 2009; Dunne et al., 2016, Pierce 2017). Already 30 years ago, Pittock (1978) discussed the statistical issues relevant to many published studies investigating the relationship between solar variations and weather or climate. He discussed many and varied claims that have been made over many years for a relationship between weather or climate and solar variations, notably sunspot cycles. Especially those relating primarily to the single and double sunspot cycles (of about 11 and 22 year quasi-periodicities) have been critically reviewed in the light of what is known about solar variations, the observed variability of weather and climate, and possible physical connections between the two. Laken et al., (2012) examined evidence of a cosmic ray cloud link from a range of sources,

including

satellite-based

cloud

measurements

and

long-term

ground-based

climatological measurements. They have presented evidence from ground-based studies suggesting some weak but statistically significant CR-cloud relationships at regional scales

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involving mechanisms related to the global electric circuit. Some statistical issues that produced conflicting results are also presented in Laken and Čalogović (2013). They examined the application of the composite (superposed epoch) analysis in the investigation of possible impacts of Forbush decrease events on cloud cover datasets. They showed how a composite may be objectively constructed to maximize signal detection, robustly identify statistical significance, and quantify the lower-limit uncertainty related to hypothesis testing. Additionally, they also demonstrated how a seemingly significant false positive may be obtained from non-significant data by minor alterations to methodological approaches. They argued that the conflicting findings of composite studies within this field relate to methodological differences in the manner in which the composites have been constructed and analysed. 3.3 Cloud cover and global surface temperature The climate system is very much influenced by various meteorological processes that change rapidly in time and space. One among these is cloud cover variation that alter the heat balance of the Earth`s climate system significantly from seasonal/decadal timescales (Kristjansson and Kristiansen 2000; Erlykin et al., 2010). The presence of low clouds is associated with a cooler surface on average but due to the increased greenhouse gases, significant uncertainties are involved in assessing the role of cloudiness changes in the climate system. In our analysis, we have shown that the cloud cover and the global surface temperature have two-way causation. As the air warms in the midyear it is likely to be drier and less cloudy because evaporation lags the temperature increase (Clayson and Bogdanoff, 2013).The cloud cover increases in the northern hemisphere as the atmosphere warms but the loss of cloud in the southern hemisphere as the south cools is much greater than the gain of cloud in the northern hemisphere. On a global basis cloud cover changes during midyear by 3%, a heavy loss of high level cloud in the southern hemisphere (Hartmann et al., 2001; Scherer et al., 2004). 4. Results and Discussion

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The intensity of cosmic rays varies globally by about 15% over a solar cycle because of changes in the strength of the solar wind which carries a weak magnetic field into the heliosphere and modulates the low energy galactic charged particles (Kitaba et al., 2017).Svensmark and Friis-Christensen (1997) have reported the correlation between the cosmic rays and the cloud cover over a solar cycle. Already a small change in cloud cover has a significant effect on the Earth's radiation balance (Sloan and Wolfendale, 2013) and provides a basis for amplifying mechanism where CR modulated by solar activity may influence Earth’s weather and climate (Marsh and Svensmark, 2000b).The globally averaged data of Nimbus 7 Earth Radiation Budget (N7ERB)experiment and the Earth Radiation Budget Experiment (ERBE) have revealed that the cloudiness reduces the input of solar radiation by 44.5-54.3 Wm−2 (depending on the seasons) and the emission of long wave radiation to space by 23.6-34.7 Wm−2 (Harrison et al., 1990; Ardanuy et al., 1991). Figure 2shows the monthly variations of the total solar irradiance, cosmic ray intensity, low cloud cover and global surface temperature during 1983 –2018while Figure 3 shows the monthly variations of MEI, NAO and QBO parameters in the same period. On the basis of this input, the empirical results are calculated and presented within a pair-wise Granger causality test in order to examine whether one variable `Granger cause`to the other one and vice versa. We have used the software Eviews 10. The Granger causality test is basically based on the rejection or the acceptance of the null hypothesis. If the p-value (probability) is less than 5% null hypothesis is not rejected but rather accepted. If the p-value is more than 5%, we reject the null hypothesis meaning that we accept alternative hypothesis. The detailed results obtained from pair-wise Granger causality test are listed in Table1. Firstly we have checked the causality between the TSI and the cosmic ray intensity. The first null hypothesis that ‘Cosmic ray influences TSI’ with the p-value 19.83% (>5%), it simply means that the hypothesis is rejected and it further states that the cosmic ray intensity does not 14

have any influence over the TSI. The second null hypothesis ‘TSI influences Cosmic ray’, the pvalue is 2.40% (< 5%), so the hypothesis is accepted and also meaning that the TSI have some influence over the variations in the cosmic ray intensity. The CR changes are connected to solar wind variations which follow the solar radiation changes (Kitaba et al., 2017). Thus correlation between CR and TSI appears. Granger causality between TSI and CR flux is a manifestation of the situation: when CR and TSI variations are driven by the solar activity changes, a common diving factor. Granger causality can appear even without any real connection between CR and TSI. The TSI doesn’t influence directly the flux of cosmic rays. SA modulates intensity of GCR by: (a) diffusion caused by scattering of GCR on the irregularities of the interplanetary magnetic field carrying by solar wind; (b) drift modulation of GCR caused by changes of the polarity of the solar magnetic field; (c) outward convection of the solar magnetic field. Most likely the relationship, obtained by the authors, is a result of synchronism in variations of TSI and the phenomena mentioned above (solar wind, interplanetary magnetic field etc.). Table1 also indicates the results of causal relationship between the cloud cover and the TSI. Here the null hypothesis that ‘Cloud cover influences TSI’ has indicated the p-value 56.47% (>5%) so we cannot accept the null hypothesis. This means that the cloud cover does not have any impact on TSI. Further, the next null hypothesis ‘TSI influences Cloud cover’ has probability equal to 4.80% which is less than 5%. Therefore, we can accept the null hypothesis meaning that the variations in the TSI have some small influence on the cloud cover. Similarly, the cause-effect relationship is investigated for other parameters and listed in Table 1 categorically. Figure 4 depicts the above obtained results from the Granger causality test. Here analysed parameters are shown in the circles and the black arrow arrows represent the direction of the Granger causality. As mentioned above we have established that the solar activity have impact 15

over variations in the cosmic ray intensity; the cloud cover; and the global surface temperature anomaly simultaneously. Figure 4 has also indicated that the cosmic ray intensity has influence over the cloud cover. Further, bi-directional causality exists between the cloud cover and the global surface temperature i.e. both have influence over each other. Additionally we have also observed the effect of natural internal oscillations (ENSO, NAO and QBO) on cloud cover. As the next step in the analysis, we adopted the variance decomposition method using Vector Auto Regression model on the considered physical parameters and the results are depicted in Figures 5-9. The variance decomposition of the cosmic ray intensity is shown in Figure 5. Since we have analysed 35 years data so have assumed 35 periods as the long run and 3 periods for the short run. The contribution of the TSI in variation of the cosmic ray is about 0.65±0.02% in short run (3 years) but in case of long run (35 years) the TSI index has contributed about 8.77±0.42% to the variation of the cosmic ray intensity. Figure 6 shows the variance decomposition of the cloud cover and its dependency in the variations of the TSI, the cosmic ray intensity as well as the global surface temperature. Considering the short run impacts of the TSI, it has contributed 0.66±0.01% fluctuations in the variance of the cloud cover but for the long run this contribution increased up to 1.68±0.03%. The cosmic ray intensity has contributed 0.55±0.01% to the fluctuation of the cloud cover in the short run and contributed 4.89±0.08% in the long run. The figure has also indicated that the surface temperature have maximum impact on the cloud cover as 1.06±0.06% the short run and has contributed 10.87±1.41% in the long run. In figure 7 we have analysed the contribution of ENSO, NAO and QBO in the cloud cover variation. In contribution of natural internal oscillations e.g. ENSO, NAO and QBO in cloud cover variation for short term period is 2.86±0.12%, 1.97±0.09% and 0.02±0.01% but for the long term these variations were reported as 7.48±1.02%, 5.51±0.16% and 1.36±0.43% respectively. The effects of the stratospheric Quasi Biennal Oscillation (QBO) on cloud cover were small because it dominates the variability of the

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tropical lower stratospheric meteorology and it also controls stratospheric ozone and water variability that can modulate surface ultra-violet (UV) and infrared (IR) radiation, whereas ENSO and NAO are strong natural fluctuation that dominate in the lower tropospheric regions (Eleftheratos et al., 2007). Hence the region of dominance provides an indication that QBO has almost no effect on clouds compared to the other climate indices ENSO and NAO. Figure 8 shows the variance decomposition of the global surface temperature and we observe that the TSI and the cloud cover have played important role in the variation of the global surface temperature. In the short run, the TSI and the cloud cover has caused 0.36±0.08% and 0.84±0.04% fluctuations in the global surface temperature respectively while the long run contribution of these two parameters increased to the values 5.07±0.47% and 14.42±2.13% respectively. Further we have analysed some connection between global surface temperature and climate oscillations as ENSO, QBO and the NAO. Figure 9 shows the contribution of ENSO, NAO and QBO in the variation of global surface temperature. The contribution of these internal oscillations e.g. ENSO, NAO and QBO in global surface temperature variation for short term period is 1.53±0.42%, 0.05±0.01% and 0.43±0.03% but for the long term these variations were reported as 5.57±1.36%, 1.63±0.64% and 1.57±0.74% respectively. Bhargawa and Singh (2019) using variance decomposition method to examine the causal relationship between the TSI and global surface temperature anomaly have predicted that the impact of solar irradiance on the global surface temperature level in next decade will increase approximately by 4.7%.Variations in the total solar irradiance are often discussed but the variations in cloud cover are not discussed at that level (Keihl, 1994; Crowley, 2000). Goode and Pallé (2007) have briefly discussed the variations in the cloud cover as part of the larger paper focusing largely on the variations in solar radiation. Herman et al. (2012) have discussed about the global cloud cover but dealt mainly with the surface reflectivity at 340nm. Kauppinen et al. (2014) has discussed the impact of the humidity and the cloud cover on the global mean surface

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temperature. Eastman and Warren (2011) have discussed the long term trends in the cloud cover while Eastman et al. (2013) have done it on the land. 5. Conclusions The observation of correlations between the cosmic ray intensity and the cloudiness offers an opportunity to try to understand the cause-effect interactions. Since presented analysis is based on the long-term and averaged data over various geographical regions (Figure 2) for cloud cover and global surface temperature there is possibility that numerous other processes could contribute to the observed variability or results could be also biased due to various calibration or measurement errors (e.g. in the case of cloud cover). The question of cloud covercosmic ray links remains controversial and requires further experimental and theoretical studies, to evaluate a real contribution of the galactic cosmic rays to solar activity influence on the Earth’s atmosphere and climate. The data analysis presented in this papers how the possible links between solar activity (TSI, cosmic rays) and climate parameters (cloud cover and global surface temperature) covers the period 1983-2018.The Granger causality analysis of four parameters namely TSI index, cosmic ray intensity, global monthly cloud cover and global surface temperature anomaly has been carried out in order to assess whether there are any potential predictability power of one indicator to the other. The dynamic relationships among the variables considered for observing the impact of one parameter to the other are examined based on Vector Auto Regression (VAR) model using the variance decomposition method. We have noticed that the TSI has contributed about 8.77±0.42% to the variation in cosmic ray intensity. In case of the cloud cover, we have found that its variation is dependent on TSI, cosmic ray and global surface temperature. Variation in TSI has contributed about 1.68±0.03% fluctuations in the variance of the cloud cover while the cosmic ray intensity has contributed 4.89±0.08%. The global surface temperature has maximum impact of about 10.87±1.41% to the fluctuation in cloud cover. In case of the

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global surface temperature anomaly, both the TSI and the cloud cover play a substantial role and have contributed 5.07±0.47% and 14.42±2.13% respectively. We have also examined on a global scale the variability of the cloud cover that can be attributed to known internal climate oscillations such as ENSO, QBO and the NAO. The effect of ENSO, QBO and NAO has been examined separately. ENSO, QBO and NAO fluctuations were found to explain significant part of the cloud variability. While QBO was found to have a very small effect on clouds, the effect of ENSO and NAO is more significant. In case of global surface temperature variation, the effect of these internal oscillations is significant. ENSO have shown largest contribution to surface temperature variation as compared to NAO and QBO. In the long term, NAO and QBO have shown approximately similar contributions. AcknowledgementsAB is thankful to University Grants Commission (UGC), India for providing financial support (Rajiv Gandhi National Fellowship). Authors are thankful to all data providers. Thanks also go to learned reviewers for their critical and fruitful comments. References Alexeenko, V.V., Khaerdinov, N.S., Lidvansky, A.S., Petkov, V.B., 2002.Transient variations of secondary cosmic rays due to atmospheric electric field and evidence for pre-lightning particle acceleration. Physics Letters A 301, 299-306. Almeida, J., Schobesberger, S., Kurten, A., Ortega, I.K., Kupiainen-Maatta, O., et al., 2013. Molecular understanding of sulphuric acid-amine particle nucleation in the atmosphere. Nature 502, 359–363. Ardanuy, P.E., Stowe, L.L., Gruber, A., Weiss, M., 1991. Shortwave, long wave and net cloudradiative forcing as determined from Nimbus 7 observations. Journal of Geophysical Research 96, 18537-18549. Atri, D., Melott, A.L., Thomas, B.C., 2010. Lookup tables to compute high energy cosmic ray induced atmospheric ionization and changes in atmospheric chemistry. Journal of Cosmology and AstroparticlePhysicsJCAP05. Ball, W.T., Unruh, Y.C., Krivova, N.A., Solanki, S., Wenzler, T., Mortlock, D.J., Jaffe, A.H., 2012. Reconstruction of total solar irradiance 1974–2009. Astro and Astrophy.541, A27. Beer, J., Blinov, A., Bonani, G., Finkel, R.C., Hofmann, H.J., Lehmann, B., Oeschger, H., Sigg, A., Schwander, J., Staffelbach, T., Stauffer, B., Suter, M., Wötfli, W., 1990. Use of 10Be in polar ice to trace the 11-year cycle of solar activity. Nature 347, 164. Bhargawa, A., Singh, A.K., 2019. Solar irradiance, climatic indicators and climate change – An empirical analysis. Advances in Space Research 64, 271–277. Bhargawa, A., Yakub, M., Singh, A.K., 2019.Repercussions of solar high energy protons on ozone layer during super storms.Research Astronomy Astrophysics19, 02-10. 19

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Caption to Table

Table1

Detailed results of the pair-wise Granger causality test

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Caption to Figures Figure 1

Temporal variation in cosmic ray intensity observed during the period 1983 to2018

Figure 2

Temporal variations of Total solar irradiance, cosmic ray intensity, global average cloud cover, and global surface temperature anomaly for the period 1983 to 2018.

Figure 3

Temporal variations of Multivariate ENSO index (MEI), North Atlantic Oscillation (NAO) and Quasi Biennial Oscillation (QBO) for the period 1983 to 2018

Figure 4

Pictorial representation of the causal relationships obtained from the Granger causality test where arrow indicates the direction of causality

Figure 5

Percentage contribution of TSI obtained from the variance decomposition method, responsible for the variation in cosmic ray intensity (red curve)

Figure 6

Percentage contributions of TSI, cosmic ray intensity and global surface temperature obtained from the variance decomposition method, responsible for the variation in cloud cover (curves are represented in red, blue and orange colours respectively) Percentage contributions of Multivariate ENSO index (MEI), North Atlantic Oscillation (NAO) and Quasi Biennial Oscillation (QBO) obtained from the variance decomposition method, responsible for the variation in cloud cover (curves are represented in black, blue and red colours respectively)

Figure 7

Figure 8

Percentage contributions of TSI, cloud cover obtained from the variance decomposition method, responsible for the variation in global surface temperature anomaly (curves are represented in red and green colours respectively)

Figure 9

Percentage contributions of Multivariate ENSO index (MEI), North Atlantic Oscillation (NAO) and Quasi Biennial Oscillation (QBO) obtained from the variance decomposition method, responsible for the variation in global surface temperature anomaly (curves are represented in black, blue and red colours respectively)

25

Fig. 1

26

Fig. 2

27

Fig.3

28

Fig. 4

29

Fig. 5

30

Fig. 6

31

Fig.7

32

Fig.8

33

Fig. 9

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Table 1

Null Hypothesis Cosmic ray influences TSI

Probability 0.1983

TSI influences Cosmic ray Cloud cover influences TSI

0.0240 0.5647

Accepted Rejected

TSI influences Cloud cover Global surface temperature influences TSI

0.0480 0.1620

Accepted Rejected

TSI influences Global surface temperature Cloud cover influences Cosmic ray

0.0366 0.0909

Accepted Rejected

Cosmic ray influences Cloud cover MEI influences Cloud cover

0.0274 0.0283

Accepted Accepted

Cloud cover influences MEI NAO influences Cloud cover

0.0940 0.0147

Rejected Accepted

Cloud cover influence NAO QBO influences Cloud cover

0.9470 0.0420

Rejected Accepted

Cloud cover influences QBO Global surface temperature influences Cosmic ray

0.8207 0.8279

Rejected Rejected

Cosmic ray influences Global surface temperature Global surface temperature influences Cloud cover

0.2589 0.0066

Rejected Accepted

Cloud cover influences Global surface temperature MEI influences Global surface temperature

0.0100 0.0157

Accepted Accepted

Global surface temperature influences MEI NAO influences Global surface temperature

0.5610 0.0315

Rejected Accepted

Global surface temperature influence NAO QBO influences Global surface temperature

0.8741 0.0486

Rejected Accepted

Global surface temperature influences QBO

0.0389

Accepted

35

Status Rejected

Highlights:

 Associations of solar activity parameters and some terrestrial climate parameters are established  Thirty five years (1983 - 2018) data of these parameters are analyzed  Granger-causality test is applied to assess potential predictability power of one indicator to other  The percent contributions of one parameter in the variation of other parameter are evaluated  Impact of internal climate oscillations on cloud cover variations are studied

Declaration of interests

[✓]The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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