GEOPHYSICAL RESEARCH LETTERS, VOL. 38, L24811, doi:10.1029/2011GL049764, 2011
Solar irradiance, cosmic rays and cloudiness over daily timescales
Benjamin A. Laken1,2 and Jasa Čalogović3
Received 23 September 2011; revised 15 November 2011; accepted 22 November 2011; published 29 December 2011.
[1] Although over centennial and greater timescales solar
variability may be one of the most influential climate forcing
agents, the extent to which solar activity influences climate
over shorter time periods is poorly understood. If a link
exists between solar activity and climate, it is likely via a
mechanism connected to one (or a combination) of the following parameters: total solar irradiance (TSI), ultraviolet
(UV) spectral irradiance, or the galactic cosmic ray (GCR)
flux. We present an analysis based around a superposed
epoch (composite) approach focusing on the largest TSI
increases and decreases (the latter occurring in both the presence and absence of appreciable GCR reductions) over daily
timescales. Using these composites we test for the presence
of a robust link between solar activity and cloud cover over
large areas of the globe using rigorous statistical techniques.
We find no evidence that widespread variations in cloud
cover at any tropospheric level are significantly associated
with changes in the TSI, GCR or UV flux, and further conclude that TSI or UV changes occurring during reductions
in the GCR flux are not masking a solar-cloud response.
However, we note the detectability of any potential links is
strongly constrained by cloud variability. Citation: Laken,
B. A., and J. Čalogović (2011), Solar irradiance, cosmic rays and
cloudiness over daily timescales, Geophys. Res. Lett., 38,
L24811, doi:10.1029/2011GL049764.
1. Introduction
[2] The Sun is one of the most important factors responsible for governing climate change over centennial and
greater timescales [e.g., Versteegh, 2005; Bard and Frank,
2006; Beer et al., 2006], however the extent to which solar
activity may influence Earth’s climate over shorter time
periods is still poorly understood. Several mechanisms have
been proposed which may account for a solar-climate link,
including: a connection between changes in total solar irradiance (TSI) absorbed over cloud free regions of Earth’s
oceans, leading to modifications of synoptic circulation
patterns [Meehl et al., 2009]; a link between ultraviolet (UV)
spectral irradiance changes and stratospheric temperatures
resulting from alterations to stratospheric ozone production
[Austin et al., 2008], which may impact large scale tropospheric variability via dynamic stratosphere-troposphere
couplings [Haigh, 1996]; and, a link between the galactic
1
Instituto de Astrofísica de Canarias, Universidad de La Laguna,
Tenerife, Spain.
2
Department of Astrophysics, Faculty of Physics, Universidad de La
Laguna, Tenerife, Spain.
3
Hvar Observatory, Faculty of Geodesy, University of Zagreb, Zagreb,
Croatia.
Copyright 2011 by the American Geophysical Union.
0094-8276/11/2011GL049764
cosmic ray (GCR) flux and cloud properties, via either an
ion-mediated (clean-air) pathway, or a global electric circuit
(GEC) related (near-cloud) pathway [Carslaw et al., 2002].
The response times of these mechanisms ranges from minutes (for mechanisms concerning the GEC) [Tinsley et al.,
2001], to periods of around a week (for mechanisms
concerning the growth of cloud condensation nuclei or
dynamic tropospheric links) [Arnold, 2007]. It should also
be noted that irradiance-based mechanisms have been suggested which operate over long (annual-decadal) timescales
(e.g. [White et al., 2003]), which are beyond the scope of
this work.
[3] These mechanisms all have the potential to amplify
relatively low energy changes in solar activity into climatologically significant effects. Such an amplification is necessary
to account for the findings of palaeoclimatic reconstruction
studies which have demonstrated the existence of a pervasive
association between solar activity and numerous climatological parameters [e.g., Ram and Stolz, 1999; Bond et al., 2001;
Mauas et al., 2011].
[4] To investigate the possibility of daily timescale solarclimate links a number of investigations have focused on the
effects of high amplitude short term reductions in the GCR
flux (known as Forbush Decrease (FD) events) on cloud
properties. However, these studies have shown a range of
conflicting results. While some have demonstrated the
presence of significant positive correlations between cloud
changes and solar activity [Pudovkin and Veretenko, 1995;
Todd and Kniveton, 2004; Harrison and Ambaum, 2010],
others have shown significant negative correlations [Wang
et al., 2006; Troshichev et al., 2008], or found no compelling evidence of significant correlations [Pallé and Butler,
2001; Kristjánsson et al., 2008; Čalogović et al., 2010;
Laken et al., 2009, 2011].
[5] A number of possibilities may account for these
conflicting findings: 1) no relationship exists between solar
activity and climate; 2) a relationship exists, but it is constrained by the atmospheric conditions at the time (i.e. not a
first order relationship) [Laken et al., 2010]; 3) even at daily
timescales studies have often failed to properly isolate the
effects of various solar parameters, consequently this may
have interfered with their results [Laken et al., 2011]; 4) FD
studies deal with inherently small sample sizes, as the
number of high magnitude solar events is low and consequently sample noise is high, limiting the detectability of
any solar-cloud signals. With regards to addressing the
third possibility, this work presents an analysis of the
largest daily timescale TSI variations using an epochsuperposition (composite) approach to test for the presence
of reliable daily-timescale link to satellite-detected cloud
variability. Samples have been carefully selected to isolate a range of periods undergoing substantial changes in
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solar activity of hypothesized significance to atmospheric
variability.
2. Datasets
[6] This investigation uses measurements of TSI emissions, cloud cover, the GCR flux, and the 10.7 cm solar
radio flux (F10.7). TSI data are taken from the Active Cavity
Radiometer Irradiance Monitor (ACRIM) [Willson and
Mordvinov, 2003].
[7] Variations in solar irradiance are caused by the presence of surface features such as sunspots and faculae, which
differ in brightness to the average surface intensity and
cumulatively contribute to fluctuations in TSI. Variability at
UV wavelengths is often connected with the presence of
plages: these are large bright complexes connected to magnetically active regions in the chromosphere. The rotation of
features such as plages and sunspots across the solar disk
alters the amount of energy emitted towards the Earth (for
further details see Lean and Woods [2010]). Changes in the
GCR flux are linked to the solar wind and associated disturbances (such as coronal mass ejections CMEs). As the
solar wind travels at supersonic speeds, variations in the
GCR flux resulting from solar disturbances can take up to
2–4 days to occur [Brueckner et al., 1998], whereas irradiance associated changes are experienced on Earth almost
instantaneously following a solar event.
[8] Cloud data are taken from the International Satellite
Cloud Climatology Project (ISCCP) D1 dataset infrared (IR)
channels [Rossow and Schiffer, 1991]. ISCCP data are created from inter-calibrated radiance measurements recorded
from polar orbiting and geostationary satellites. These data
are provided globally over on an equal-area grid of 280 280 km2, at a 3-hour temporal resolution from 1983–2008.
In this investigation daily average cloud data (retrieved at IR
wavelengths) is used at 3 different altitude levels: for high
(>6.5 km), middle (2–6.5 km) and low (0–3.2 km) clouds.
At each of these altitude levels a further distinction is also
drawn between high (>45°) and low (<45°) latitude regions
as well as over ocean and land regions. This area-averaging
method was selected both in order to reduce noise associated
with daily cloud variability and because the theoretical
effects of solar irradiance and GCR flux on cloud cover may
vary across spatial domains.
[9] GCR flux data are derived from the count rate recorded by the Climax Colorado neutron monitor (39.37 N,
106.18 W, 3400 m, 2.99 GV). The F10.7 (2800 Mhz)
radio flux is used as a proxy of extreme ultraviolet (EUV)
solar activity [Rich et al., 2003]. All used data (except the
F10.7) have been normalized to a static averaging period
from 20 to 10 days prior to the key composite date (i.e.
all values displayed are an anomaly calculated against a
10-day static averaging period).
3. Methods
[10] Three composite samples were constructed for this
analysis. The first sample isolates dates of significant
increases in TSI occurring over a five day period where the
increase is half of the key date in magnitude and shall
hereafter be referred to as IncTSI. The second sample isolates dates of significant decreases in TSI over a five day
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period, and will be referred to as DecTSI-A. The third sample
referred to as DecTSI-B is identical to the DecTSI-A sample,
however it is further restricted to events which show no
significant GCR variations within 10 days of the key
composite date.
[11] The composited events were selected from a population of the largest (95 percentile) daily timescale increases/
decreases in TSI from 1978 to 2010. To calculate the changes in the TSI record, a seven-day running mean was subtracted from a 31-day reference period, these original
populations were then reduced by excluding consecutive
dates, leaving only the date of greatest TSI deviation.
Finally, events were also removed from the populations if
significant TSI deviations (at least half of the key date in
magnitude) were observed to occur within a 10 day period
around the key date. This treatment further reduced the
samples sizes to 19 events for the IncTSI sample, 48 events
for the DecTSI-A sample and 37 events for the DecTSI-B
sample presented in Table S1 in the auxiliary material.1
[12] The correlation coefficient (r) values were calculated
for each three samples during an analysis period of 20 days
between the TSI, GCR and F10.7 fluxes and the corresponding cloud data. The analysis period of the cloud data
was extended by an additional 20 days allowing us to calculate the correlations with a lag of up to 20 days. Monte
Carlo (MC) cased testing was employed to establish the
threshold significance values for every obtained correlation:
random composite samples using the whole available cloud
dataset (ISCCP, 1983–2008) were constructed with sample
sizes corresponding to the sample they were testing (e.g. n =
19 random events for the IncTSI sample). These random
composites of cloud data were correlated with every investigated parameter, and this process was repeated 100,000
times. The resulting r values were found to be normally
distributed, according to the Shapiro-Wilk test of normalcy
(W = 0.996, p = 4.8 10 10) [Shapiro and Wilk, 1965].
Consequently, the statistical significance thresholds for this
work are set by the two-tailed 95 percentile MC-generated
r values. This approach shows what a stochastic range of
correlations should be given random sampling. If the solar
variations of our samples were to affect cloud changes then
the obtained correlations would be out of this stochastic
range and a significant correlation would be detected.
[13] Consequently, this implies that for a solar-cloud
signal to be detected the efficiency of the mechanism must
be high (where efficiency here refers to the ability of a
change in a solar parameter to influence a change in cloud,
e.g. if a 1% change in the GCR flux induced a 1% change
in cloud cover the efficiency is 100%): for example, in the
case of low level clouds over ocean area regions we find
that for 100,000 randomly generated samples of 48 events,
there is an average sample noise of 0.83(0.15)% over a
41-day period (Table 1). Thus, to detect a statistically
significant TSI-cloud correlation with this level of noise
under the IncTSI sample (TSI increase of 0.08%), a
mechanism would have to have an efficiency greater than
([0.83/0.08]*100=) 1,038%. Whereas, in the case of the
1
Auxiliary materials are available in the HTML. doi:10.1029/
2011GL049764.
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Table 1. Simulated Standard Deviations of Cloud Data for
Various Regionsa
Sample
n = 48
n = 37
n = 19
LC - <45°
LC - >45°
MC - <45°
MC - >45°
HC - <45°
HC - >45°
LC - Land
LC - Ocean
MC - Land
MC - Ocean
HC - Land
HC - Ocean
0.95(0.18)
1.75(0.35)
1.22(0.23)
1.44(0.32)
1.51(0.28)
3.30(0.62)
2.57(0.60)
0.83(0.15)
1.59(0.37)
1.11(0.20)
2.50(0.47)
1.83(0.34)
1.09(0.21)
1.99(0.40)
1.38(0.26)
1.63(0.36)
1.72(0.33)
3.75(0.72)
2.92(0.68)
0.95(0.17)
1.80(0.41)
1.26(0.23)
2.84(0.53)
2.09(0.39)
1.51(0.29)
2.78(0.56)
1.93(0.38)
2.27(0.50)
2.40(0.46)
5.20(1.03)
4.04(0.95)
1.33(0.24)
2.50(0.57)
1.75(0.33)
3.95(0.75)
2.90(0.57)
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mirror the TSI emissions, showing an increase of 40% on
the key date (Figure 1c).
[16] The DecTSI-B sample (denoted in all figures by green
solid lines) showed TSI changes that corresponded closely
to the DecTSI-A sample, although the peak reductions in
the TSI emissions were of a marginally lower magnitude
(Figure 1a). GCR reductions on day +3 are only 0.3%; an
order of magnitude lower than those identified in the
DecTSI-A sample and are considerably smaller than daily
a
Standard deviations (1.96s level) of 100,000 Monte Carlo simulations
of ISCCP cloud data at various pressure levels: low altitude (LC), middle
altitude (MC), and high altitude (HC), over low latitude (<45°), high
latitude (>45°), ocean and land regions. Error range is shown in brackets.
These values provide an estimate of the sample noise, to which any solar
induced cloud changes must be greater than in order to be detectable.
Values are presented for every composite size (n = 48, 37, 19).
DecTSI-A sample, with a GCR reduction of 2.8%, a GCR
mechanism would have to have an efficiency greater than
30% to produce a detectable signal.
4. Results and Discussion
[14] Figure 1 shows the variation in TSI, GCR and F10.7
occurring over the three composite samples. The IncTSI
sample (denoted by blue lines in all figures) showed an
increase in TSI emissions of 0.076% centered around
the key date, beginning on day 4 before decreasing to
approximately original values by around day +5 (Figure 1a).
This sample displayed no strong GCR fluctuations over the
composite until day +8, where a reduction in the GCR flux
of 1.5% is observed over a two day period, followed by a
fast recovery and another additional lower magnitude
reduction on day +16 (Figure 1b). Closer inspection of the
individual events revealed that only a few events are
responsible for the GCR flux reductions (such as 26/08/
1989, 06/03/1991, and 19/05/1991). The F10.7 shows a
steady increase of 20% following the key date over a seven
day period; values were then seen to plateau for 3 days
before declining to undisturbed levels (Figure 1c). The F10.7
index shows a delay in changes of around one week. This
delay corresponds to the journey time of the active regions
from the solar limb to solar disc centre. Faculae responsible
for TSI variations have their greatest influence at the solar
limb [Carlsson et al., 2004], whereas plages (responsible for
variations in the F10.7 index) have their greatest influence
near the centre of the solar disc. A close inspection of the
individual events in the IncTSI composite reveals that four
events (07/06/2989, 13/05/1990, 22/01/1991 and 09/08/1991)
are contributing to a large portion of the F10.7 increase.
[15] The DecTSI-A sample (denoted in all figures by red
dashed lines) showed a strong decrease in TSI emissions of
0.13% centered on the key date over a five day period
(Figure 1a). It also showed a stronger reduction in the GCR
flux of 2.8%, centered on day +3 (Figure 1b) which can be
explained by the travel time of the solar wind disturbances to
the Earth. Variations in the F10.7 were found to inversely
Figure 1. Solar activity parameter variations over composite
samples. This figure displays the mean variations of TSI
flux, GCR flux, and the F10.7 index for the three composite
samples: the IncTSI sample, which shows the largest (95 percentile) increases in TSI (displayed on the blue line, n = 19);
the DecTSI-A sample, which shows the largest decreases in
TSI (displayed on the green dotted line, n = 48); and, the
DecTSI-B sample, which shows the largest decreases in TSI
with significant GCR reductions excluded (displayed on the
red dashed line, n = 37). The mean fluctuations in the individual parameters are shown for individually for the three
samples as follows: (a) TSI changes, (b) the GCR flux, and
(c) the F10.7 EUV index. Shaded regions indicate the standard error of the mean (at the 95 two-tailed confidence level).
All values are an anomaly calculated against a 10-day static
averaging period beginning on day 20.
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Figure 2. Cloud variations at high and low latitudes. Normalized ISCCP cloud cover changes (%) at (a and b) low levels
(0–3.2 km), (c and d) middle levels (3.2–6.5 km), and (e and f ) high levels (>6.5 km). Data are presented at low (<45°) and
high (>45°) latitudes, over a 20 to +40 day period surrounding the key composite date. IncTSI sample denoted by blue line,
DecTSI-A sample denoted by green dotted line, and DecTSI-B sample denoted by red dashed line. All values are an anomaly
calculated against a 10-day static averaging period beginning on day 20.
GCR variations (Figure 1b). The F10.7 values were virtually
identical to the previously described DecTSI-A sample
(Figure 1c).
[17] None of the samples showed any significant correlations between cloud anomalies and the TSI, GCR or F10.7
flux for any of the investigated regions (high, low latitudes
or ocean and land regions) at any altitude level (low, middle
and high) within the 20-day lag period (Figure 2 and
Figure S1). However, we note that TSI showed slightly
higher correlation coefficients than in the case of the GCR of
F10.7 flux. Slightly higher, but non-significant correlation
coefficients were also noticed in the case of ocean regions
of the DecTSI-B sample compared to land regions, giving
some support for the mechanism suggested by Meehl et al.
[2009]. Closer inspection of the spatial distributions of
correlation coefficients for TSI, GCR and F10.7 across
the globe (not shown) revealed inhomogeneous sporadic
correlations, with no field significance.
[18] Additionally, no significant correlations were identified between any dataset (TSI/GCR/F10.7) and globally
averaged cloud cover, where the correlations were somewhat
lower than those achieved by regional samples. This may be
explained by the observation of the existence of correlations/
anti-correlations in the cloud data (particularly high cloud
between low and high latitude regions), which (assuming the
changes represent absolute changes in cloud amount, as
opposed to shifts in cloud cover between the latitude divisions) may cancel out when considering a global area.
[19] It is theoretically plausible that a correlation between
solar activity and cloud may exist over smaller regions than
those considered in this work. However, reliably detecting
low amplitude signals with the data used in this study is
highly problematic. This is primarily due to the fact that
cloud datasets show a high degree of variability; the higher
the spatial resolution of the experiment, the greater this issue
becomes. This is demonstrated in Table 1, which displays
MC simulated 95% confidence level cloud variations at low
middle and high cloud levels for low and high latitude
regions across the composite samples; in each instance the
variations are found to strongly increase with decreasing
region and sample size.
[20] This important observation indicates that the detectability of any potential signal over a given sample will be
strongly constrained by the size of the area, and the size of
the sample, and may account for the wide-ranging and
conflicting results obtained by workers dealing with such
studies. We essentially have attempted to attain a balance
between area-averaging (as a method of minimizing sample
noise), with selecting physically meaningful sample areas
with respect to theoretical solar-climate linkages. The aim
was to select our sample areas to reflect regions that may
allow us to both detect a solar signal and attribute it to a
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mechanism, whilst minimizing potential noise-to-signal ratio
issues.
5. No Evidence of a GCR-Based Solar-Cloud Link
[21] Comparing the cloud anomalies of the DecTSI-A and
DecTSI-B samples (TSI reductions in the presence and
absence of significant GCR reductions) yields no appreciable differences (Figure 1b). The same was observed in the
case of ocean and land regions (Figure S1), where due to the
different conditions low clouds cover ocean regions are
claimed to be more sensitive to GCR changes [Yu and
Turco, 2001; Marsh and Svensmark, 2003]. From this we
conclude that in the case of the selected events the variations
in the GCR flux do not significantly alter widespread cloud
amount at any tropospheric level.
[22] Large TSI reductions have been found to accompany
short term reductions in the GCR flux, and it has been
suggested that the co-variations in these parameters may
complicate the unambiguous attribution of changes in atmospheric properties to a specific cause, or even interfere with
the detectability of potential signals [Laken et al., 2011],
possibly influencing the results of studies [e.g., Pudovkin and
Veretenko, 1995; Todd and Kniveton, 2004; Svensmark et al.,
2009]. However, the analysis presented in this work shows
that following careful isolation of TSI and GCR variations,
neither is found to be significantly associated with changes in
cloud cover.
[23] Recent results from the Cosmic’s Leaving Outdoor
Droplets (CLOUD) experiment at CERN has shown evidence that the GCR flux may enhance the formation of
aerosols by ion-mediated nucleation [Kirkby et al., 2011].
However, the enhancement is low, implying that for the
majority of tropospheric conditions the enhanced nucleation
rate is not large enough to ultimately affect cloud condensation nuclei (CCN) numbers significantly. Such conclusions have also been made independently based on climate
model results [Pierce and Adams, 2009]. The results we
present here are in agreement with these findings, as we find
no significant change in cloud properties following significant GCR fluctuations. Furthermore, our selection of certain
regions (e.g. low and high latitudes) and altitude levels
support the findings of the CLOUD experiment, which
demonstrates that temperature and altitude play a primary
role in determining ion induced aerosol nucleation [Kirkby
et al., 2011].
6. Conclusions
[24] This work has attempted to test the notion of a link
between solar activity and cloud cover, using several highly
isolated composite samples. These samples reflected periods
of increasing and decreasing TSI, the latter being in the
presence/absence of significant reductions in the GCR flux.
Although we successfully isolated periods of significant
solar activity changes, we found no widespread detectable
changes in cloud cover at any tropospheric level within a
20 day period of the solar forcing clearly associated with
solar activity changes. Thus we can also conclude that TSI
or UV changes occurring during reductions in the GCR flux
are not masking a solar-cloud response. It is still possible
that any small amplitude or low efficiency solar-cloud signals may be hidden by high meteorological variability of the
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cloud data, although the sample selection and large area
averages utilized provide some compensation for this effect.
[25] Acknowledgments. The authors thank Bojan Vršnak (Hvar
Observatory), Enric Pallé (Instituto de Astrofísica de Canarias) and
Dominic Kniveton (University of Sussex) for comments. ACRIM data
obtained from http://www.acrim.com/Data%20Products.htm. ISCCP data
are available from http://isccp.giss.nasa.gov, obtained from NASA Langley
Research Centre Atmosphere Science Data Center. This research received
funding from the European 115 Commission’s Seventh Framework Programs (FP7/2007-2013), grant 116, 218816. We also thank Geoffrey
Tyndall and two anonymous reviewers. The authors would like to acknowledge the support of the European COST Action ES1005.
[26] The Editor thanks two anonymous reviewers for their assistance in
evaluating this paper.
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J. Čalogović, Hvar Observatory, Faculty of Geodesy, University of
Zagreb, Kaciceva 26, HR-10000 Zagreb, Croatia.
B. A. Laken, Instituto de Astrofísica de Canarias, Via Lactea s/n,
E-38205, La Laguna, Tenerife, Spain. ([email protected])
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