TYPE Original Research
PUBLISHED 19 July 2023
DOI 10.3389/feart.2023.1157753

OPEN ACCESSEDITED BY

Siming Liu,
Southwest Jiaotong University, China
REVIEWED BY

Sergey Alexander Pulinets,
Space Research Institute (RAS), Russia
Mirela Voiculescu,
Dunarea de Jos University, Romania*CORRESPONDENCE

Hiroko Miyahara,
miyahara@musabi.ac.jp
RECEIVED 03 February 2023

Response of high-altitude clouds
to the galactic cosmic ray cycles intropical regions
Hiroko Miyahara 1,2*, Kanya Kusano 3, Ryuho Kataoka 2,4,5,
Shin-ichiro Shima 6 and Emile Touber 2,7
1
Humanities and Sciences/Museum Careers, Musashino Art University, Tokyo, Japan, 2Okinawa Instituteof Science and Technology, Okinawa, Japan, 3Institute for Space-Earth Environmental Research, Nagoya
University, Nagoya, Aichi, Japan, 4National Institute of Polar Research, Tachikawa, Japan, 5SOKENDAI, The
Graduate University for Advanced Studies, Hayama, Kanagawa, Japan, 6Graduate School of Information
Science, University of Hyogo, Kobe, Japan, 7Department of Mechanical Engineering, Imperial College
London, London, United Kingdom

ACCEPTED 06 July 2023
PUBLISHED 19 July 2023
CITATION

Miyahara H, Kusano K, Kataoka R, Shima S
and Touber E (2023), Response of highaltitude clouds to the galactic cosmic ray
cycles in tropical regions.
Front. Earth Sci. 11:1157753.
doi: 10.3389/feart.2023.1157753
COPYRIGHT© 2023 Miyahara, Kusano, Kataoka, Shimaand Touber. This is an open-accessarticle distributed under the terms of theCreative Commons Attribution License(CC BY). The use, distribution or
reproduction in other forums ispermitted, provided the original author(s)and the copyright owner(s) are credited
and that the original publication in thisjournal is cited, in accordance withaccepted academic practice. No use,
distribution or reproduction is permitted
which does not comply with these terms.

Galactic cosmic rays are one of the possible mediators of the solar inﬂuence on
climate. However, the impacts of GCR on clouds and climate systems are not fully
understood. In this paper, we show that the high-altitude clouds associated withdeep convective activities are responding to the decadal-scale cycles of GCRs andthat the susceptible areas are seasonally variable. Most notable responses were
found in August over tropical land areas, suggesting that the susceptivity of clouds
to GCRs depends on the depth of convective activities and the abundance ofaerosol precursor materials. Furthermore, following the activation of high-altitude
cloud formation, an increase in sea surface temperature (SST) gradient wasobserved over the Paciﬁc. Although the response of sea surface temperature
to solar activity has mostly been discussed as mediated by solar radiations, we
propose that another mechanism is possible: through the impact of GCRs on
clouds and the resultant changes in atmospheric circulations.
KEYWORDS

sun-climate connection, cosmic rays, clouds, atmospheric circulation, sea surface
temperatures

1 Introduction
The possible responses of climate to solar activity variations have been reported forvarious time scales (Gray et al., 2010), and several mediators have been proposed,
including solar radiations (Kodera & Kuroda, 2002; Matthes et al., 2006; Meehl et al.,2008; Misios et al., 2019), GCRs modulated by solar-wind magnetic ﬁeld (Svensmark &Friis-Christensen, 1997; Carslaw et al., 2002), and the interplanetary magnetic ﬁeld
(Voiculescu et al., 2013; Scott et al., 2014). Notable responses of climate, such astemperatures and precipitation, to solar activity have been observed for millennial (Bondet al., 2001; Obrochta et al., 2012) and centennial time scales (Neff et al., 2001; Wanget al., 2005); however, identifying the relative importance of mediating solar-activity
related parameters is difﬁcult at such time scales, as the radiative and magnetic outputsof the Sun vary in a similar pattern. To identify the contribution of each of theparameters and trace the propagation of their impacts, it is needed to examine theshorter time scales, such as those associated with the solar decadal cycle, or even shorter,
where the temporal variation of the solar radiative outputs and GCRs are slightly
different (Miyahara et al., 2008; Yamaguchi et al., 2010).

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also favorable in terms of the abundance of GCR-induced ions (Ney,
1959; Ermakov et al., 1997; Usoskin et al., 2004). Deep convection isa possible method for supplying aerosol precursors from thebiogenic activities at the ground or ocean surfaces to the upper
troposphere (Twohy et al., 2002; Kazil et al., 2006); therefore, thehigh-altitude clouds near highly convective areas are potentiallymost susceptive to GCRs, although the deep convection may also
contribute to the transport of newly-formed cloud condensation
nuclei to the lower troposphere to change the cloud properties(Williamson et al., 2019).
The impact of GCRs through the formation of aerosols may only
be emphasized if there are few pre-existing aerosols in an ambientenvironment (Almeida et al., 2013), as newly formed aerosols tend tobe adsorbed to pre-existing aerosols if they are abundant.
Atmospheric aerosols, including the ones that haveanthropogenic origins, are mostly conﬁned within ~4 km fromthe surface, except over the mountains with high elevations(Kofﬁ et al., 2016). This factor also suggests a possibility that
only the middle to upper troposphere meets the criteria of
signiﬁcantly being impacted by GCRs.
In this paper, we examine the response of high-altitude clouds
based on records over the past 43 years. Due to the possible longterm artifactual inﬂuence from satellite-based observations (Evanet al., 2007), it is crucial to examine the cloud behaviors based on
multiple independent data sets, and, to concentrate on the short to
mid-term ﬂuctuations. Therefore, in this work, we base on two
records (see methods) and focus on the response at the decadal scale.
We used monthly-resolved high temporal-resolution data toconstrain the possible conditions required for cloud activity torespond to GCR variations.

Solar radiations vary based on the emergence and disappearanceof sunspots and faculae on the solar surface (Domingo et al., 2009).
Therefore, they change along with the decadal-scale variation of theactivity level of sunspots. However, the ﬂux of GCRs incident to the
Earth’s atmosphere is attenuated by the solar wind magnetic ﬁeld in
the heliosphere and is thus dependent on the evolution of the
conﬁguration and its direction (Jokipii & Thomas, 1981). As aresult, the ﬂux of GCRs is dependent on the solar magnetic
polarity that reverses every solar cycle maximum (seeSupplementary Figure S1). In addition, the transient
intensiﬁcation of the magnetic ﬁelds associated with solar coronalmass ejections contributes to the shielding of GCRs (Forbush, 1938).
Due to the travel time of the solar magnetic ﬁeld in the heliosphereand its inﬂuence on the trajectory of GCRs, the variation of GCRs at
Earth occasionally delays up to ~1.4 years relative to the decadal
variations in solar activity level (Usoskin et al., 2001; Koldobskiy
et al., 2022). Such features might allow identifying the potential
contribution of GCRs to the Sun–Climate connection.
The possible impact of the decadal-scale solar activity cycle on
climate has been reported, e.g., in the North Atlantic region (Kodera,
2002; Gray et al., 2016; Kuroda et al., 2022), in the Paciﬁc region(Dima & Voiculescu, 2016), and particularly in the tropical region(Gleisner and Thejll, 2003; van Loon et al., 2004; White, 2006; Misios
et al., 2019). Recent studies have suggested that an increased solar
activity results in a reduction in the east-west gradient of SST overthe Paciﬁc and in a weakening of the Paciﬁc Walker Circulation(Misios et al., 2019). These decadal-scale Sun-Climate connections
have been mostly attempted to be explained by the so-called “topdown” mechanism, through which solar UV (SUV) inﬂuences
stratospheric temperature and subsequently alters troposphericcirculation (Kodera & Kuroda, 2002; Matthes et al., 2006) or by
the “bottom-up” mechanism, through which the total solar
irradiance (TSI) warms up the ocean to change atmosphericcirculation (Meehl et al., 2008; Misios et al., 2019). However,signiﬁcant positive feedback is needed for the latter mechanism
to explain the observed temperature variations, as the variability of
TSI over solar cycles is as small as 1 W/m2.
It is, however, also possible that GCRs contribute to the decadalscale Sun–Climate connection through the ionization and its
impacts on cloud condensation nuclei (Dickinson, 1975; Carslaw
et al., 2002; Kirkby et al., 2011; Svensmark et al., 2013), by enhancing
the collision efﬁciency between aerosols and cloud droplets (Tinsley,
2000; Zhou et al., 2009; Tinsley, 2022), or by stabilizing themolecular cluster to grow to cloud condensation nuclei (Tinsley
and Deen, 1991; Yu & Turco, 2001; Yu, 2002). However, it is not wellunderstood where their effects may proceed in actual environments
and how those impacts propagate in the climate system.
Originally, it was suggested that the cloud covers over oceans areenhanced with the increase in GCRs (Svensmark & FriisChristensen, 1997). Later on, it was demonstrated that the lowaltitude clouds over oceans are most signiﬁcantly correlated to GCRvariations (Marsh & Svensmark, 2003). Voiculescu et al. (2006) has
suggested that the correlation to low-altitude clouds is signiﬁcant
over the mid latitudes of the Atlantic. However, both theoreticalestimates and the laboratory chamber experiment have indicatedthat GCR-induced aerosol formations are rather efﬁcient at low
temperatures (Kazil et al., 2006; Yu et al., 2008; Kirkby et al., 2011;
Dunne et al., 2016) (i.e., at high altitudes). The upper troposphere is

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2 Methods
To examine the response of high-altitude clouds to GCR decadal
cycles, we utilized a daily record of Outgoing Longwave Radiation(OLR) (Lee and NOAA CDR Program, 2001) with 1° × 1° resolution
for Jan/1979–Dec/2021. OLR reﬂects the existence of high-altitude
clouds, although only for low-latitude regions. For example, theexistence of high clouds with low cloud-top temperatures leads tolower OLR value. In this study. We calculated the fraction of the days
OLR is equal to or lower than a threshold value for each month. We
produced four time series for each 1° × 1° grid with a threshold value:
170 W/m2, 200 W/m2, 230 W/m2, and 260 W/m2, respectively. In
cases where the threshold used yielded a fraction of none or 100% for
more than ﬁfty percent of the time window used in the analysis, thethreshold was considered too low or too high for that 1° × 1° tile andwas discarded. The time series were then compared with the GCRvariation to derive the Spearman’s correlation coefﬁcient. The
maximum correlation coefﬁcient among the four cases and thecorresponding threshold were displayed on the maps. In thetropical regions, if the maximum correlation was obtained withthe threshold of 200 W/m2, it implies that tropospheric high-altitude
clouds (cloud top pressure ≤ ~440 mb) are most sensitively
responding to GCRs. On the other hand, if the maximum
correlation was obtained with 230 W/m2, it suggests that adding
mid-altitude clouds (~440 mb < cloud top pressure ≤680 mb)
improves the correlation.

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10.3389/feart.2023.1157753(ISCCP) H-series Gridded Monthly (HGM), were compared with
GCR cycles (see Methods). Then, it was found that both seriesconsistently indicate that there are regions in the tropics where highaltitude clouds show signiﬁcant positive correlations to decadalscale GCR cycles (Figures 1, 2, also see Supplementary Figures S2,
S3), suggesting that GCRs may be contributing to the changes incloud activity. However, these areas are localized and vary based on
the seasons. Most signiﬁcant correlations were found in August for
the areas in which the formation of high-altitude clouds is active (seeFigure 1), supporting the above-mentioned hypothesis; however,they were localized to the land areas and nearby oceans. There werealso some regions in which high cloud formations were suppressed(see below). In boreal winter, the areas showing signiﬁcantcorrelations migrated to the convective regions in the southernhemisphere (Figure 2, also see Supplementary Figure S3). The
correlations were weaker compared with those of August;however, a prompt response was observed around the northern
tip of Australia and the northwest coast of South America(Figure 2A). Figures 1F–I and Figures 2F–I indicate the OLR
range that achieved the maximum correlation for each grid.
While the threshold of 200 W/m2 or lower suggests that the
response is limited to the high-altitude clouds, the threshold of
230/m2 or higher implies that the addition of mid- and possibly
lower altitude clouds improves the correlation. For example,
Figure 1F and Figure 2F suggest that the correlations off thenorthwest coast of South America involve the response of midaltitude clouds.
In August, the correlations were maximized in 1 year (Figure 1B)and diminished afterward (Figures 1C, D). Such lagged responses ofclouds imply that a positive feedback mechanism exists behind the
GCR–cloud connection (see the ﬁfth paragraph in the Discussion).
The correlations around the Indonesian maritime continent were
maximized with a further delay (Figures 1C, D), suggesting an
impact through the mechanism involving atmospheric and ocean
coupling. Similarly, the correlations around the northern tip ofAustralia and the northwest coast of South America in January
diminished after 1 year, whereas the correlations around theIndonesian maritime continent were maximized in 2–3 years(Figures 2C, D).
The maximum variability of the ratio of existence days ofhigh-altitude clouds over the GCR cycle is shown in Figure 1J
and Figure 2J. The obtained maps indicate that there are regions
where the variability is much larger than expected from the ion
production rate in the tropics (see Supplementary Figure S12 ofDunne et al., 2016), also supporting the existence of a positive
feedback mechanism. For example, while the variability of ion
production rate in the upper troposphere is up to ~20% around
20–30 °N and 20–30 °S in the tropics and is smaller in the lower
latitude regions, the variability of the fraction of days OLR isequal to or lower than 200 W/m 2 is larger than 20% in August
around eastern India and Bangladesh (Figure 3), where themean fraction is ~40% (Figure 1E), although thecorresponding variability of cloud amount needs further
investigations. Note that the enhancement in the fraction ofdays with the presence of high clouds as estimated based on the
OLR thresholds may also be caused by the uplift of theconvective cloud system in addition to the increase of cloudamount itself.

The response of clouds to GCR may be accompanied by sometime lags; therefore, correlations were examined for lags between
0 and 3 years. When estimating correlation coefﬁcients with a time
lag of 0 years, correlation coefﬁcients with lags of −2 years (GCRs lag
cloud variation with 2 years) to 0 years (no time lag) were calculated,and only the cases the correlation was maximized at 0 years wereconsidered non-false correlation and displayed on the map. For thezero-year lag, the cloud data were compared with the monthly mean
GCR ﬂux and with the yearly mean for a lag of 1 year or longer. With
the lag and threshold that yielded the maximum correlation
coefﬁcient, we estimated, based on the regression line, themaximum variability of high-altitude clouds over the GCR cycles,i.e., the variability for 1987–1990 when GCR variation was at its
maximum. For the GCR variation, the neutron monitor data for Jan/
1953–Nov/2006 obtained at the Climax station (http://cr0.izmiran.
ru/clmx/main.htm) and those for Apr/1964–Dec/2021 obtained at
the Oulu station (http://cr0.izmiran.ru/oulu/main.htm) were used.
The daily data were normalized and averaged to obtain the monthly
means. Prior to the analyses, the long-term trends were subtracted
from the cloud and GCR data to concentrate on the decadal-scalevariations.
We also analyzed the ISCCP-HGM series provided by theInternational Satellite Cloud Climatology Project (Rossow et al.,2016) to validate the response of OLR to GCRs. We used themonthly data of high (≤440 mb) and low (>680 mb) cloud
fractions for July/1983–June/2017.
For the examination of the response of SST, we used the NOAA
Optimum Interpolation SST V2 data provided by NOAA/OAR/ESRL PSL (Reynolds et al., 2002). We used the 1-degree grid data for
Dec/1981–Dec/2021. We also used the Niño 4 index (Trenberth andStepaniak, 2001) and the NCEI Paciﬁc Decadal Oscillation index(Mantua, 1999). To analyze the response of surface pressure, zonal
wind, and meridional wind, we used the JRA-55 (Japanese 55-year
Reanalysis) data of monthly mean pressure reduced to mean sea
level with 1.25° × 1.25° resolution (Kobayashi et al., 2015). We onlyused data from 1979 when the observational data was substantial
and the reliability was high (Ebita et al., 2011). For the precipitation
analysis, we used CMAP monthly mean precipitation data with
2.5° × 2.5° resolution (Huffman et al., 1997).
To examine the responses of SST and atmospheric data to TSI,
the NOAA Climate Data Record of TSI (Coddington et al., 2015)
was used. As an index of solar UV, NOAA adjusted the solar radio
ﬂux at 10.7 cm (https://lasp.colorado.edu/lisird/data/noaa_radio_ﬂux/) were combined with the Penticton radio ﬂux data for May/
2018 to present (https://lasp.colorado.edu/lisird/data/penticton_
radio_ﬂux/).
Note that the data from Jun/1991 to May/1993 were excluded
from the analyses so that the possible impacts from the eruption of
Mt. Pinatubo in 1991 are eliminated.

3 Results
3.1 Relationship between high-altitude
clouds and GCR cycles
The monthly data of high-altitude clouds, as monitored by OLR
and those of International Satellite Cloud Climatology Project

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FIGURE 1(A–D) Correlation coefﬁcient r (p ≤ 0.05) between the ratio of existence days of high-altitude clouds and GCRs in August for a time lag of 0 (no timelag) to 3 years (clouds lag GCRs). (E) Fraction of the days OLR is ≤200 W/m2 in August. (F–I) OLR ranges that yielded the maximum correlation coefﬁcientsin (A–D). (J) Maximum variability of the ratio of existence days of high-altitude clouds over the GCR cycles.

3.2 Relationship between SST and the GCR
cyclesis more or less the same for TSI (see Supplementary Figure S4) and ispeaked with a lag of ~3 years.
The areas showing signiﬁcant correlations between SST and
GCRs with no time lag were limited to the southern edge of thetropical zone around 20–30°S 100–130°W (Figure 4A); however, theimpacts on SST were expanded and maximized with a lag of 2 years(Figure 4C). The maximum temperature change around theequatorial region over the GCR cycle was as large as 1.7 K and
was observed in an area centered at about 180°W(E) (Figure 4G, also
see Supplementary Figure S5 for the time proﬁle). Regarding
January, the east–west contrast was less well structured. However,
the maximum change around the region reached 2.1 K (seeSupplementary Figure S6). This region is often characterized by
the El Niño modoki events (Ashok et al., 2007) and has been
examined using the Niño 4 index, one of the indices of the El
Niño–Southern Oscillation. Although the Niño 4 index is derived
based on the SST over the region wider than those indicatingcorrelations to GCRs, and thus the correlation coefﬁcient
between Niño 4 and GCRs is relatively lower, the lead-lag

Figures 4A–E indicates the correlation coefﬁcient between SST
and GCRs in August when the most notable correlations were foundfor cloud activity. Figure 4F exhibits the spatial pattern of SST in
August. The ﬁgures indicate that decadal-scale forcing results in acharacteristic spatial pattern in the central and western Paciﬁc.
While the SST in the central Paciﬁc tends to decrease as GCR isenhanced, especially in the winter hemisphere (Figure 4G), the SST
in the southwestern Paciﬁc tends to be increase, suggesting that thetrade winds over the Paciﬁc region are intensiﬁed at the GCR cycle
maxima. This tendency is consistent with the previously suggested
reduced east–west SST gradient and the weaker trade winds at thesolar cycle minima (Misios et al., 2019). However, the response of
SST to TSI, which was suggested as the forcing parameter in Misios
et al. (2019), delays by 1 year compared to the response to GCR, andthe correlations between SST and TSI were maximized with a lag of
3 years (see Figures 4H–N). The relationship between SST and SUV

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FIGURE 2(A–J) Same as Figure 1 but for January.

the Niño 4 index and the Paciﬁc Decadal Oscillation index showsthat they are linked with an occasional lag of up to 1 year (seeSupplementary Figures S7E, F).

analysis supports that the decadal component of SST in this region
lags that of GCRs by about 2 years (see Supplementary Figures
S7A, B).
Figures 4C, G indicates that the areas showing correlation with
GCRs include the Bering Sea, which is within the region
characterized by the Paciﬁc Decadal Oscillation (Mantua et al.,1997), and that the correlations become maximum with a lag of
2 years. The lead-lag analysis between the Paciﬁc Decadal Oscillationindex and the GCRs shows that the correlation becomes maximum
when the lag is about 2–3 years (see Supplementary Figures S7C, D),
supporting that the decadal component of the Paciﬁc Decadal
Oscillation also lags that of GCRs.
In the cases the decadal components of the Niño 4 and the
Paciﬁc Decadal Oscillation indices were compared to the tropicalhigh-altitude clouds, correlations were observed with a spatial
pattern similar to those of Figures 1A, B; however, they were
maximized when the lag was −2 to −1 years (see Supplementary
Figures S8, S9), supporting that the decadal components in the
Paciﬁc Decadal Oscillation and the Niño 4 indices lag those oftropical cloud activities. Note that the direct comparison between

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3.3 Relationship between the surfacepressure, zonal/meridional winds, and GCR
cycles
The comparison between the surface pressure and GCRs
(Figures 5A–F) indicates increased pressure around the southernedge of the tropical zone in the Paciﬁc (Figure 5A), and the impacts
are further intensiﬁed and expanded toward the northernhemisphere in 1–2 years (Figures 5B, C). On the contrary, thetropical regions between 120°W and 100°E indicate a tendency of
decreasing pressure for the higher GCR, especially over the oceans.
The zonal and meridional wind speed compared with GCRs suggests
a possible intensiﬁcation of trade winds or a westerly migration of
the deep convection core around the western Paciﬁc, especially inthe northern hemisphere (see Supplementary Figure S10). When the05

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FIGURE 3
Variability of the ratio of existence days of high-altitude clouds as monitored by OLR with a threshold value of 200 W/m2 (pale blue line) and the 3point running averages (blue line) for the area shown in the map (highlighted by cyan), plotted together with the anomaly of GCR ﬂux monitored byneutron monitors (red line) and TSI (green line). The variability of ion production rate in the upper troposphere over the solar cycles is about 20% aroundthe region (Dunne et al., 2016). Note that all of the series are high-pass ﬁltered for p ≤ 15 yrs. Note also that the period of 2 years after the eruption of
Mt. Pinatubo, indicated by the gray highlight, was excluded from the correlation analysis of this study.

positive GCR-cloud correlations were found in tropical regions;however, they are concentrated over land and nearby oceans,suggesting the importance of any of or all the following factors:
1) the presence of relatively deeper convections compared withoceans, 2) the abundance of continental aerosol precursors for ionsto produce aerosols, and 3) a more pronounced diurnal cycle overlands (see below). Most notable correlations were found in August
around West and Central Africa, India and Bangladesh, thenorthwest coast of South America, and the proximate oceans,with a lag of 0–1 years (Figures 1A, B). The correlations aroundeastern India and Bangladesh suggest that the sea breezes blowing
toward elevated mountains may also contribute to creating anenvironment in which cloud formations become sensitive to
GCRs. They uplift a substantial amount of water vapor andaerosol precursors to the upper troposphere, similar to deep
convection. The correlations around the southern Brazil in
February (see Supplementary Figure S11) may also be related tothe same mechanism. Even though convective cloud formation is
active over Brazil in austral summer, the correlations were notsigniﬁcant except for the areas facing oceans, thus suggesting theimportance of marine aerosol precursors for the impact of ions.
The more pronounced impact in August, compared with
January, can be associated with the relatively low pressurearound the convective areas in August (Figure 5E, also seepressure data were compared with TSI, slightly different behaviors
were recognized (Figures 5G–L). One is the absence of immediate
response of pressures (Figure 5G), and the other is the overall delayin the responses (Figures 5H–J) compared with the case for GCR(Figures 5A–C).

4 Discussion
Although the inﬂuence of solar cycles on climate has so far beenmostly discussed under the framework of the “top-down” or“bottom-up” mechanisms described earlier, the present results
suggest that another mechanism is possible: “deep-convectiveclouds-mediated” mechanism through the inﬂuence of GCRs on
the development of deep convective clouds, and their impact on
atmospheric circulation and SST gradient.
The monthly-resolved high-resolution data allowed us to
identify the areas where high-altitude clouds are responding to
GCR variations and to understand the possible contributing
factors determining their susceptivity, although high-resolution
analyses might fail to capture the responses of the clouds that are
not stationed and randomly advected after being formed or those
whose locations are under the inﬂuence of other interannual
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FIGURE 4(A–E) Correlation coefﬁcient r (p ≤ 0.05) between GCRs and SST in August for a lag of 0–4 years. (F) Monthly mean SST for August. (G) Maximum
variability of SST over the GCR cycles. (H–N) Same as (A–G) but for TSI.

Supplementary Figure S12), which provides improved conditions
for supplying water vapor and aerosol precursors to the upper
troposphere. In other words, the overlap of the Intertropical

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Convergence Zone (ITCZ) with the continental areas could be
the key to strengthening the GCR-cloud connection. The
signiﬁcant northward excursion of ITCZ from the geomagnetic

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FIGURE 5(A–D) Correlation coefﬁcient r (p ≤ 0.05) between surface pressure and GCRs in August for a lag of 0–3 years. (E) Monthly mean pressure reduced tothe mean sea level for August. (F) Maximum variability of surface pressure over the GCR cycles. (G–L) Same as (A–F) but for TSI.

only a few percent. The more signiﬁcant impact in August may also
be related to the seasonal variability in the emission of organic
compounds from biogenic activities, the precursory materials for theaerosol formation (Kirkby, 2007; Almeida et al., 2013). For example,
the ﬂux of dimethyl sulﬁde is maximum in the northern hemispherefrom July to September and is especially enhanced around the northpart of the Indian Ocean, near the continental areas (Land et al.,2014).
Although the climatological condition is similar for July andAugust, the correlations between high clouds and GCRs areequator in August also contributes in terms of the magnitude of thevariability in ion production rate. As also mentioned in the Results
section, the variability of the abundance of GCR-induced ions isgreater at higher latitudes, especially at high altitudes (seeSupplementary Figure S12B of Dunne et al., 2016); thus, theexcursion of ITCZ signiﬁcantly increases the encounters between
ions and aerosol precursors. The lower pressure in August also
contributes to the higher GCR ﬂux in the troposphere due to the
reduced barometric effect (Myssowsky and Tuwim, 1926; De
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reduced formation of high-altitude clouds over the western
Paciﬁc (Figures 1A, B) can be associated to the westward
relocation of deep convections around the area. Low-altitude
clouds, instead, are likely increased around the western Paciﬁc(see Supplementary Figures S15G, H), consistent with thepreviously found correlation between GCRs and low-altitude
clouds in this region (Marsh and Svensmark, 2003). It is worth
noting that this is a region of typhoon generation (Bloemendaal
et al., 2020). While less typhoon activity is suggested for the higher
GCR ﬂux at this region, more high-altitude clouds are expected forthe higher GCR ﬂux around the areas where hurricanes are
generated, as suggested by the positive relationship over the low
latitude regions of the North Atlantic (Figure 1B).
The westward extension of trade wind over the Paciﬁc eventually
warms the ocean around Indonesian maritime continent and off thenortheast coast of Australia, and this warming is maximized with a lagof 2–3 years (Figures 4C, D). The enhancement of high-altitude clouds
around the area with a lag of 2–3 years can be related to this increased
SST. The correlation between the GCRs and the SST in the northernpart of the Paciﬁc Ocean with a lag of ~2 years suggests that the alteredatmospheric circulation pattern may also eventually contribute tomodulating the Paciﬁc Decadal Oscillation, although the mechanism
behind the connection to the Paciﬁc Decadal Oscillation remains
unknown and thus needs further examination.
The responses of atmospheric circulation and SST to the GCR
cycles are similar to those suggested as a response to TSI cycles inprevious studies; however, there are two notable differences. The ﬁrst is
the overall slower responses of atmospheric circulation and SST to TSI
than when compared to GCR (Figures 4H–L; Figures 5G–J), consistent
with the ~1-year delay of GCRs to TSI. The second is the warming of theeastern Indian Ocean as an immediate response to TSI (Figures 4H, N).
This feature, however, contradicts the weakening of the easterly wind in
the western Paciﬁc and the cooling tendency around the region
suggested for the TSI maxima, as seen for the lag of 2–4 years(Figures 4J–L). Instead, it is more likely that this warming is related
to the positive response of this region to GCR with a lag of ~4 years(Figure 4E), which is a remnant of the impact around the Indonesian
maritime continent (Figure 4D). Note that the areas responding
positively to GCR with a lag of 4 years could indicate an apparent
negative correlation to TSI with a lag of ~5.4 years because GCR lags
~1.4 years behind and correlates inversely with TSI. Five years are thennearly 180 degrees of a decadal solar cycle; thus, it could result in an
apparent immediate positive response to TSI.

signiﬁcantly different. The impact in July is sparse and not notable
for a lag of 0–1 years (see Supplementary Figures S13A, B), while
correlations become pronounced around the Indonesian maritime
continent for a lag of 2–3 years (see below). The possible explanation
for the relatively weaker response in July may be related to theinﬂuence of the updrafted pre-existing aerosols masking the impact
of GCRs. For example, the abundance of mineral dust in northern
Africa is maximum in June and starts to decrease in July
(Vandenbussche et al., 2020). It has also been reported that theaerosol optical depth in northern India is maximum in May and thatit starts to decrease in July (Gautam et al., 2010). Further
examinations are, however, needed to conﬁrm the impact of preexisting aerosols.
The tendency of the decreased pressure around tropical zones
except for the Paciﬁc region (Figures 5A–C) can be related to theactivated formation of deep convective clouds, and it may be causing
positive feedback to the promotion of cloud activity by the GCRs byenhancing the encounters between ions and aerosol precursory
materials. It has been suggested that aerosol particles could
prolong the lifetime of deep convective clouds by enhancing
smaller ice crystals with smaller fall velocities (Grabowski &Morrison, 2020) and by increasing freezing water droplets toenhance the release of latent heat (Rosenfeld et al., 2008),
resulting in the extension of associated anvils. Then, the extended
thin anvil clouds over the proximate area would increase the net
radiative forcing (warming) (Koren et al., 2010). It has been
speculated that the enhancement of latent heat release may evenstrengthen deep convection (Rosenfeld et al., 2008), although thisfactor may only be signiﬁcant for the altitudes below the freezing
level (Grabowski & Morrison, 2020). The latent heat release is also
expected to increase in the case the collisional processes in clouds arepromoted by the electriﬁcation of aerosols and cloud drops by GCRs
(Tinsley and Deen, 1991). Enhancement of latent heat thencontributes to stronger and/or longer updrafts. The synchronizedprolongation/activation of convections over land in tropical regions
should result in a tendency of decreased pressure around the area.
Note that while the correlations between clouds and GCRs wereobserved most signiﬁcantly at the high altitudes, the process behind
the intensiﬁcation of deep convective cloud activities may also act at
the middle layer of deep convective clouds. As mentioned in the
Introduction, deep convection may transport the newly-formed
cloud condensation nuclei to the lower altitudes.
The pressure decrease is more prominent over oceans and issigniﬁcantly weaker over land (Figure 5B), and this might be related
to the more pronounced diurnal cycle over land (Yang and Slingo,
2001), which may mask the signals of the transient pressure
decreases in monthly averaged data. However, the diurnal cycle
over land is probably playing an essential role in sustaining
convective activity and supplying aerosol precursors to the upper
troposphere, even under enhanced cloud formation. In fact, theprecipitation pattern indicates increased precipitation around theareas where high-altitude clouds are increased (see Supplementary
Figure S14), supporting this tendency. Increased precipitation mightalso contribute to removing pre-existing aerosols from theatmosphere.
The changed pressure gradient then affects atmosphericcirculation (see Supplementary Figure S9), allowing the change in
the SST gradient over the Paciﬁc Ocean (Figures 4A–E). The

Frontiers in Earth Science

5 Concluding remarks
The possible solar inﬂuence pathway on climate systems throughthe variation of GCRs can be summarized as follows. First, GCRs
impact the deep convective cloud activities in the tropics, primarily overthe land areas, resulting in a decrease in pressure around the area,
possibly giving positive feedback to cloud formation. Second, thereduced pressure intensiﬁes atmospheric circulations and changes
the SST pattern over the Paciﬁc. Finally, the altered SST patternactivates the high-altitude cloud formation around the Indonesian
maritime continent. Note that although the suggested characteristic
response of clouds to GCRs seems to support the existence of GCR’s
impact through the formation of aerosols, it is possible that they also

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10.3389/feart.2023.1157753

Author contributions

affect clouds by the other paths, such as promoting the collisions
between aerosols and cloud droplets (Tinsley, 2000; Zhou et al., 2009;
Tinsley, 2022). Further investigations on the response of deep
convective clouds to the GCR variations, especially over tropicalland areas, should contribute to elucidate the contribution of ions,
possibly through multiple mechanisms, and to realize the physics-based
simulations to quantitatively evaluate the GCR’s impacts on cloud
activities and global climate.
It is noteworthy that no correlation was observed in SST around theeastern Paciﬁc region, where the El Niño–Southern Oscillation is mostprominent. It was, however, found that the areas showing response to
GCRs include the regions where periodic behaviors are often observed in
SST, such as El Niño Modoki, the Indian Ocean Dipole, and the Paciﬁc
Decadal Oscillation. It is, therefore, possible that the GCRs may enhancethe variability of the decadal component in such periodic behaviors, butwith one to a few years of time lag. Further investigations on the proposed
impacts of GCRs on cloud activity and atmospheric circulation may shedlight on the variability or the phase changes of the decadal-scale
components in such unresolved oceanic variations.

HM designed the study with input from KK and HM performed
data analyses and wrote the manuscript with input from all of theother authors. All authors contributed to the article and approved
the submitted version.

Funding
This work was supported by JSPS KAKENHI grand number
15H05816 and the research scholarship provided to Musashino Art
University by Mr. Haruo Suzuki.

Acknowledgments
We thank S. Masuda, H. Yamada, Y. Yamashiki, K. Munakata,for discussions. HM thanks Okinawa Institute of Science andTechnology for hosting her sabbatical visit.

Data availability statement

Conﬂict of interest

Publicly available datasets were analyzed in this study. This data canbe found here: The OLR data are available at https://www.ncei.noaa.
gov/products/climate-data-records/outgoing-longwave-radiation-daily.
ISCCP-HGM series is available at https://www.ncei.noaa.gov/products/
international-satellite-cloud-climatology. The Oulu and Climax
neutron data are available at http://cr0.izmiran.ru/clmx/main.htm
and http://cr0.izmiran.ru/oulu/main.htm, respectively. The NOAAOI SST V2 data are provided by NOAA/OAR/ESRL PSL on theirwebsite:https://psl.noaa.gov/data/gridded/data.noaa.oisst.v2.html.
Japanese 55-year Reanalysis data are available at https://rda.ucar.edu/
datasets/ds628.1/. The ESRL/NOAA Niño 4 index is available at https://
psl.noaa.gov/data/correlation/nina4.data. The NCEI Paciﬁc Decadal
Oscillation index is available at https://www.ncei.noaa.gov/access/monitoring/pdo/. CMAP Precipitation data are provided by NOAA/OAR/ESRL PSL on their website: https://psl.noaa.gov/data/gridded/
data.cmap.html. The NOAA Climate Data Record of TSI areavailable at https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00828. The data of NOAA adjusted solar
radio ﬂux at 10.7 cm is available at https://lasp.colorado.edu/lisird/data/
noaa_radio_ﬂux/. The Penticton radio ﬂux data are available at https://lasp.colorado.edu/lisird/data/penticton_radio_ﬂux/.

The authors declare that the research was conducted in theabsence of any commercial or ﬁnancial relationships that could beconstrued as a potential conﬂict of interest.

Publisher’s noteAll claims expressed in this article are solely those of the authorsand do not necessarily represent those of their afﬁliatedorganizations, or those of the publisher, the editors and thereviewers. Any product that may be evaluated in this article, or
claim that may be made by its manufacturer, is not guaranteed or
endorsed by the publisher.

Supplementary material
The Supplementary Material for this article can be found online
at: https://www.frontiersin.org/articles/10.3389/feart.2023.1157753/
full#supplementary-material

References
Carslaw, K. S., Harrison, R. G., and Kirkby, J. (2002). Cosmic rays, clouds, and climate.
Science 298, 1732–1737. doi:10.1126/science.1076964

Almeida, J., Schobesberger, S., Kürten, A., Ortega, I. K., Kupiainen-Määttä, O.,
Praplan, A. P., et al. (2013). Molecular understanding of sulphuric acid–amine
particle nucleation in the atmosphere. Nature 502, 359–363. doi:10.1038/
nature12663

Coddington, O., Lean, J. L., Lindholm, D., Pilewskie, P., and Snow, M. NOAA CDR
Program (2015). NOAA climate data record (CDR) of total solar irradiance (TSI),
NRLTSI version 2, N.O.A.A. Natl. Centers Environ. Inf. doi:10.7289/V55B00C1

Ashok, K., Behera, S. K., Rao, S. A., Weng, H., and Yamagata, T. (2007). El Niño
modoki and its possible teleconnection. J. Geophys. Res. 12, C11007. doi:10.1029/
2006jc003798

De Mendonça, R. R. S., Raulin, J.-P., Echer, E., Makhmutov, V. S., and Fernandez, G.
(2013). Analysis of atmospheric pressure and temperature effects on cosmic ray
measurements. J. Geophys. Res. Space Phys. 118, 1403–1409. doi:10.1029/2012JA018026

Bloemendaal, N., Haigh, I. D., de Moel, H., Muis, S., Haarsma, R. J., and Aerts, J. C.
J. H. (2020). Generation of a global synthetic tropical cyclone hazard dataset using
STORM. Sci. Data 7, 40. doi:10.1038/s41597-020-0381-2

Dickinson, R. E. (1975). Solar variability and the lower atmosphere. Bull. Am.
Meteorological Soc. 56, 1240–1248. doi:10.1175/1520-0477(1975)056<1240:svatla>2.0.
co;2Bond, G. C., Kromer, B., Beer, J., Muscheler, R., Evans, M. N., Showers, W., et al.
(2001). Persistent solar inﬂuence on North Atlantic climate during the holocene. Science
294, 2130–2136. doi:10.1126/science.1065680

Frontiers in Earth Science

Dima, M., and Voiculescu, M. (2016). Global patterns of solar inﬂuence on high cloud
cover. Clim. Dyn. 47, 667–678. doi:10.1007/s00382-015-2862-0

10

frontiersin.org

Miyahara et al.

10.3389/feart.2023.1157753

Mantua, M. K., Hare, S. R., Zhang, Y., Wallace, J. M., and Francis, R. C. (1997). A
Paciﬁc interdecadal climate oscillation with impacts on salmon production. Bull. Am.
Meteorological Soc. 78, 1069–1079. doi:10.1175/1520-0477(1997)078<1069:apicow>2.0.
co;2Domingo, V., Ermolli, I., Fox, P., Fröhlich, C., Haberreiter, M., Krivova, N., et al.
(2009). Solar surface magnetism and irradiance on time scales from days to the 11-year
cycle. Space Sci. Rev. 145, 337–380. doi:10.1007/s11214-009-9562-1
Dunne, E. M., Gordon, H., Kürten, A., Almeida, J., Duplissy, J., Williamson, C., et al.
(2016). Global atmospheric particle formation from CERN CLOUD measurements.
Science 354, 1119–1124. doi:10.1126/science.aaf2649

Mantua, N. J. (1999). “The paciﬁc decadal oscillation and climate forecasting for northAmerica,” in Climate risk solutions. Editor M. Golnaraghi (Seattle: University ofWashington), 10–13.

Ebita, A., Kobayashi, S., Ota, Y., Moriya, M., Kumabe, R., Onogi, K., et al. (2011). The
Japanese 55-year Reanalysis “JRA-55”: An interim report. Sola 7, 149–152. doi:10.2151/
sola.2011-038

Marsh, N., and Svensmark, H. (2003). Galactic cosmic ray and el Niño–southern
oscillation trends in international satellite cloud Climatology Project D2 low-cloudproperties. J. Geophys. Res. Atmos. 108, 2001JD001264. doi:10.1029/2001JD001264

Ermakov, V. I., Bazilevskaya, G. A., Pokrevsky, P. E., and Stozhkov, Y. I. (1997). Ion
balance equation in the atmosphere. J. Geophys. Res. Atmos. 102, 23413–23419. doi:10.
1029/97jd01388

Matthes, K., Kuroda, Y., Kodera, K., and Langematz, U. (2006). Transfer of the solar
signal from the stratosphere to the troposphere: Northern winter. J. Geophys. Res. 111,
D06108. doi:10.1029/2005jd006283

Evan, A. T., Heidinger, A. K., and Vimont, D. J. (2007). Arguments against a physicallong-term trend in global ISCCP cloud amounts. Geophys Res. Lett. 34, L04701. doi:10.
1029/2006gl028083

Meehl, G. A., Arblaster, J. M., Branstator, G., and van Loon, H. (2008). A coupled
air–sea response mechanism to solar forcing in the Paciﬁc region. J. Clim. 21,
2883–2897. doi:10.1175/2007jcli1776.1

Forbush, S. E. (1938). On cosmic-ray effects associated with magnetic storms.
J. Geophys. Res. 43, 203–218. doi:10.1029/TE043i003p00203

Misios, S., Gray, L. J., Knudsen, M. F., Karoff, C., Schmidt, H., and Haigh, J. D. (2019).
Slowdown of the walker circulation at solar cycle maximum. Proc. Natl. Acad. Sci. U. S.
A. 116, 7186–7191. doi:10.1073/pnas.1815060116

Gautam, R., Hsu, N. C., and Lau, K.-M. (2010). Premonsoon aerosol characterization
and radiative effects over the Indo-Gangetic Plains: Implications for regional climate
warming. J. Geophys. Res. 115, D17208. doi:10.1029/2010JD013819

Miyahara, H., Yokoyama, Y., and Masuda, K. (2008). Possible link between multidecadal climate cycles and periodic reversals of solar magnetic ﬁeld polarity. Earth
Planet. Sci. Lett. 272, 290–295. doi:10.1016/j.epsl.2008.04.050

Gleisner, H., and Thejll, P. (2003). Patterns of tropospheric response to solar
variability. Geophys. Res. Lett. 30, 1711. doi:10.1029/2003GL017129

Myssowsky, L., and Tuwim, L. (1926). Unregelmassige intensitätsschwankungen der
Hohenstrahlung in geringer seehohe. Zeitschrift für Physik A – Hadrons Nucl. 39,
146–150. doi:10.1007/bf01321981

Grabowski, W. W., and Morrison, H. (2020). Do ultraﬁne cloud condensation nuclei
invigorate deep convection? J. Atmos. Sci. 77, 2567–2583. doi:10.1175/jas-d-20-0012.1
Gray, L. J., Beer, J., Geller, M., Haigh, J. D., Lockwood, M., Matthes, K., et al. (2010).
Solar inﬂuences on climate. Rev. Geophys. 48, RG4001. doi:10.1029/2009RG000282

Neff, U., Burns, S. J., Mangini, A., Mudelsee, M., Fleitmann, D., and Matter, A. (2001).
Strong coherence between solar variability and the monsoon in Oman between 9 and6 kyr ago. Nature 411, 290–293. doi:10.1038/35077048

Gray, L. J., Woollings, T. J., Andrews, M., and Knight, J. (2016). Eleven-year solar
cycle signal in the NAO and Atlantic/European blocking. Q. J. R. Meteorological Soc.
142, 1890–1903. doi:10.1002/qj.2782

Ney, E. R. (1959). Cosmic radiation and the weather. Nature 183, 451–452. doi:10.
1038/183451a0

Huffman, G. J., Adler, R. F., Arkin, P., Chang, A., Ferraro, R., Gruber, A., et al. (1997).
The global precipitation Climatology Project (GPCP) combined precipitation dataset.
Bull. Amer Meteor Soc. 78, 5–20. doi:10.1175/1520-0477(1997)078<0005:tgpcpg>2.0.
co;2Obrochta, S. P., Miyahara, H., Yokoyama, Y., and Crowley, T. J. (2012). A reexamination of evidence for the North Atlantic “1500-year cycle” at Site 609. Quat. Sci.
Rev. 55, 23–33. doi:10.1016/j.quascirev.2012.08.008
Reynolds, R. W., Rayner, N. A., Smith, T. M., Stokes, D. C., and Wang, W. (2002). An
improved in situ and satellite SST analysis for climate. J. Clim. 15, 1609–1625. doi:10.
1175/1520-0442(2002)015<1609:aiisas>2.0.co;2

Jokipii, J. R., and Thomas, B. (1981). Effects of drift on the transport of cosmic rays.
IV. Modulation by a wavy interplanetary current sheet. Astrophysical J. 243, 1115–1122.
doi:10.1086/158675

Rosenfeld, D., Lohmann, U., Raga, G. B., O’Dowd, C. D., Kulmala, M., Fuzzi, S., et al.
(2008). Flood or drought: How do aerosols affect precipitation? Science 321, 1309–1313.
doi:10.1126/science.1160606

Kazil, J., Lovejoy, E. R., Barth, M. C., and O’Brien, K. (2006). Aerosol nucleation overoceans and the role of galactic cosmic rays. Atmos. Chem. Phys. 6, 4905–4924. doi:10.
5194/acp-6-4905-2006

Rossow, W. B., Walker, A., Golea, V., Knapp, K. R., and Young, A. (2016). NOAA’s
climate data record Program (2016). International satellite cloud Climatology Projectclimate data record. H-Series HGM NOAA Natl. Centers Environ. Inf. doi:10.7289/
V5QZ281S

Kirkby, J. (2007). Cosmic rays and climate. Surv. Geophys. 28, 333–375. doi:10.1007/
s10712-008-9030-6
Kirkby, J., Curtius, J., Almeida, J., Dunne, E., Duplissy, J., Ehrhart, S., et al. (2011).
Role of sulphuric acid, ammonia and galactic cosmic rays in atmospheric aerosol
nucleation. Nature 476, 429–433. doi:10.1038/nature10343

Scott, C. J., Harrison, R. G., Owens, M. J., Lockwood, M., and Barnard, L. (2014).
Evidence for solar wind modulation of lightning. Environ. Res. Lett. 9, 055004. doi:10.
1088/1748-9326/9/5/055004

Kobayashi, S., Ota, Y., Harada, Y., Ebita, A., Moriya, M., Onoda, H., et al. (2015). The
JRA-55 Reanalysis: General speciﬁcations and basic characteristics. J. Meteorological
Sciety Jpn. 93, 5–48. doi:10.2151/jmsj.2015-001

Svensmark, H., Enghoff, M. B., and Pedersen, J. O. P. (2013). Response of cloud
condensation nuclei (>50 nm) to changes in ion-nucleation. Phys. Lett. A 377,
2343–2347. doi:10.1016/j.physleta.2013.07.004

Kodera, K., and Kuroda, Y. (2002). Dynamical response to the solar cycle. J. Geophys.
Res. 107, 4749. doi:10.1029/2002jd002224

Svensmark, H., and Friis-Christensen, E. (1997). Variation of cosmic ray ﬂux andglobal cloud coverage—a missing link in solar-climate relationships. J. Atmos. SolarTerrestrial Phys. 59, 1225–1232. doi:10.1016/S1364-6826(97)00001-1

Kodera, K. (2002). Solar cycle modulation of the North atlantic oscillation:
Implication in the spatial structure of the NAO. Geophys. Res. Lett. 29, 59-1–59-4.
doi:10.1029/2001GL014557

Tinsley, B. A., and Deen, G. W. (1991). Apparent tropospheric response to
MeV-GeV particle ﬂux variations: A connection via electrofreezing ofsupercooled water in high-level clouds? J. Geophys. Res. 96, 22283–22296.
doi:10.1029/91JD02473

Kofﬁ, B., Schulz, M., Bréon, F. M., Dentener, F., Steensen, B. M., Griesfeller, J., et al.
(2016). Evaluation of the aerosol vertical distribution in global aerosol models throughcomparison against CALIOP measurements: AeroCom phase II results. J. Geophys. Res.
Atmos. 121, 7254–7283. doi:10.1002/2015jd024639

Tinsley, B. A. (2000). Inﬂuence of solar wind on the global electric circuit, and inferred
effects on cloud microphysics, temperature, and dynamics in the troposphere. Space Sci.
Rev. 94, 231–258. doi:10.1023/a:1026775408875

Koldobskiy, S. A., Kähkönen, R., Hofer, B., Krivova, N. A., Kovaltsov, G. A., andUsoskin, I. G. (2022). Time lag between cosmic-ray and solar variability: Sunspot
numbers and open solar magnetic ﬂux. Sol. Phys. 297, 38. doi:10.1007/s11207-02201970-1

Tinsley, B. A. (2022). Uncertainties in evaluating global electric circuit interactions
with atmospheric clouds and aerosols, and consequences for radiation and dynamics.
J. Geophys. Res. Atmos. 127, e2021JD035954. doi:10.1029/2021jd035954

Koren, I., Remer, L. A., Altaratz, O., Martins, J. V., and Davidi, A. (2010). Aerosolinduced changes of convective cloud anvils produce strong climate warming. Atoms.
Chem. Phys. 10, 5001–5010. doi:10.5194/acp-10-5001-2010

Trenberth, K. E., and Stepaniak, D. P. (2001). Indices of el Niño evolution. J. Clim. 14,
1697–1701. doi:10.1175/1520-0442(2001)014<1697:lioeno>2.0.co;2
Twohy, C. H., Clement, C. F., Gandrud, B. W., Weinheimer, A. J., Campos, T. L.,
Baumgardner, D., et al. (2002). Deep convection as a source of new particles in the
midlatitude upper troposphere. J. Geophys. Res. Atmos. 107, AAC 6-1–AAC 6-10. AAC
6–AAC 1. doi:10.1029/2001JD000323

Kuroda, Y., Kodera, K., Yoshida, K., Yukimoto, S., and Gray, L. (2022). Inﬂuence ofthe solar cycle on the North Atlantic oscillation. J. Geophys. Res. Atmos. 127, e2021.
doi:10.1029/2021jd035519
Land, P. E., Shutler, J. D., Bell, T. G., and Yang, M. (2014). Exploiting satellite
Earth observation to quantify current global oceanic DMS ﬂux and its futureclimate sensitivity. J. Geophys. Res. Oceans 119, 7725–7740. doi:10.1002/
2014JC010104

Usoskin, I. G., Gladysheva, O. G., and Kovaltsov, G. A. (2004). Cosmic ray-induced
ionization in the atmosphere: Spatial and temporal changes. J. Atmos. Solar-Terrestrial
Phys. 66, 1791–1796. doi:10.1016/j.jastp.2004.07.037
Usoskin, I. G., Mursula, K., Kananen, H., and Kovaltsov, G. A. (2001). Dependence ofcosmic rays on solar activity for odd and even solar cycles. Adv. Space Res. 27, 571–576.
doi:10.1016/s0273-1177(01)00084-9

Lee, H. T. NOAA CDR Program (2001). NOAA climate data record (CDR) of daily
outgoing Longwave radiation (OLR), version 1.2. NOAA National Climatic Data Center.
(Date of access: 15/01/2022). doi:10.7289/V5SJ1HH2

Frontiers in Earth Science

11

frontiersin.org

Miyahara et al.

10.3389/feart.2023.1157753

Yamaguchi, Y. T., Yokoyama, Y., Miyahara, H., Sho, K., and Nakatsuka, T. (2010).
Synchronized Northern Hemisphere climate change and solar magnetic cycles during
the Maunder Minimum. Proc. Natl. Acad. Sci. U. S. A. 107, 20697–20702. doi:10.1073/
pnas.1000113107

van Loon, H., Meehl, G. A., and Arblaster, J. M. (2004). A decadal solar effect in thetropics in July–August. J. Atmos. Solar-Terrestrial Phys. 66, 1767–1778. doi:10.1016/j.
jastp.2004.06.003
Vandenbussche, S., Callewaert, S., Schepanski, K., and De Mazière, M. (2020). North
African mineral dust sources: New insights from a combined analysis based on 3D dust
aerosol distributions, surface winds and ancillary soil parameters. Atmos. Chem. Phys.
20, 15127–15146. doi:10.5194/acp-20-15127-2020

Yang, G.-Y., and Slingo, J. (2001). The diurnal cycle in the tropics. Mon.
Weather Rev. 129, 784–801. doi:10.1175/1520-0493(2001)129<0784:tdcitt>2.0.
co;2Yu, F. (2002). Altitude variations of cosmic ray induced production of aerosols:
Implications for global cloudiness and climate. J. Geophys. Res. 107, 1118. doi:10.1029/
2001ja000248

Voiculescu, M., Usoskin, I., and Condurache-Bota, S. (2013). Clouds blown by thesolar wind. Environ. Res. Lett. 8, 045032. doi:10.1088/1748-9326/8/4/045032
Voiculescu, M., Usoskin, I. G., and Mursula, K. (2006). Different response of clouds tosolar input. Geophys. Res. Lett. 33, L21802. doi:10.1029/2006gl027820

Yu, F., and Turco, R. P. (2001). From molecular clusters to nanoparticles: Role ofambient ionization in tropospheric aerosol formation. J. Geophys. Res. Atmos. 106,4797–4814. doi:10.1029/2000jd900539

Wang, Y., Cheng, H., Edwards, R. L., He, Y., Kong, X., An, Z., et al. (2005). The
holocene asian monsoon: Links to solar changes and North Atlantic climate. Science
308, 854–857. doi:10.1126/science.1106296

Yu, F., Wang, Z., Luo, G., and Turco, R. (2008). Ion-mediated nucleation as animportant global source of tropospheric aerosols. Atmos. Chem. Phys. 8, 2537–2554.
doi:10.5194/acp-8-2537-2008

White, W. B. (2006). Response of tropical global ocean temperature to the Sun’squasi-decadal UV radiative forcing of the stratosphere. J. Geophys. Res. 111, C09020.
doi:10.1029/2004jc002552

Zhou, L., Tinsley, B. A., and Plemmons, A. (2009). Scavenging in weakly
electriﬁed saturated and subsaturated clouds, treating aerosol particles anddroplets as conducting spheres. J. Geophys. Res. 114, D18201. doi:10.1029/
2008JD011527

Williamson, C. J., Kupc, A., Axisa, D., Bilsback, K. R., Bui, T. P., et al. (2019). A large
source of cloud condensation nuclei from new particle formation in the tropics. Nature
574, 399–403. doi:10.1038/s41586-019-1638-9

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