A Decade of the Moderate Resolution Imaging Spectroradiometer:Is a Solar–Cloud Link Detectable?

BENJAMIN LAKEN AND ENRIC PALLE

Instituto de Astrofısica de Canarias, and Department of Astrophysics, Faculty of Physics, Universidad de La Laguna,

La Laguna, Tenerife, Spain

HIROKO MIYAHARA

Institute for Cosmic Ray Research, University of Tokyo, Kashiwa, Japan

(Manuscript received 13 June 2011, in final form 30 November 2011)

ABSTRACT

Based on the results of decadal correlation studies between the International Satellite Cloud Climatology

Project–detected cloud anomalies and the galactic cosmic ray (GCR) flux, it has been suggested that a re-

lationship exists between solar activity and cloud cover. If valid, such a relationship could have important

implications for scientists’ understanding of recent climate change. In this work, an analysis of the first decade

of Moderate Resolution Imaging Spectroradiometer (MODIS)–detected cloud anomalies are presented, and

the data at global and local geographical resolutions to total solar irradiance (TSI), GCR variations, and the

multivariate El Nino–Southern Oscillation index are compared. The study identifies no statistically significant

correlations between cloud anomalies and TSI/GCR variations, and concludes that solar-related variability is

not a primary driver of monthly to annual MODIS cloud variability. The authors observe a net increase in

cloud detected by MODIS over the past decade of ;0.58%, arising from a combination of a reduction in high-

to midlevel cloud (20.31%) and an increase in low-level cloud (0.89%); these long-term changes may be

largely attributed to ENSO-induced cloud variability.

1. Introduction

In 1997 a positive correlation was noted between var-

iations in oceanic cloud cover over geostationary sat-

ellite regions and the solar-modulated galactic cosmic

ray (GCR) flux for the period of 1983–94 (Svensmark

and Friis-Christensen 1997). In this study, cloud mea-

surements were taken from the International Satellite

Cloud Climatology Project (ISCCP) (Rossow and Schiffer

1999). The ISCCP data are an intercalibration of radi-

ance measurements, recorded by a fleet of polar-orbiting

and geostationary satellites, provided at an 8-times-daily

temporal resolution since 1983. Data from this project

have been widely utilized for studying the possibility of

a relationship between solar activity and cloud cover

(e.g., Palle and Butler 2000; Todd and Kniveton 2001;

Kristjansson et al. 2002; Laut 2003; Marsh and Svensmark

2003; Kristjansson et al. 2004; Calogovic et al. 2010;

Laken et al. 2010, 2011).

Solar activity has increased over the last century

(Lean et al. 1995; Solanki et al. 2004). Consequently, the

amount of low-energy GCRs impinging on earth’s

atmosphere has decreased during this period. This ob-

servation led Svensmark (2007) to suggest that GCR-

modulated cloud decreases may be primarily responsible

for the recent anomalously high global temperatures

and not anthropogenic emissions; such claims have also

been made by other researchers based on similar argu-

ments (e.g., Shaviv 2005; Rao 2011). However, it is un-

likely that these claims are valid, as since 1987 solar

activity levels have remained constant and thus can-

not account for the observed warming climate trends

(Lockwood and Frohlich 2008), and the historical cloud

datasets do not seem to show evidence of such GCR–

cloud connections (Palle and Butler 2002). Furthermore,

the arguments supporting an anthropogenic (and not

solar based) warming of climate over the past several

decades are more consistent with the observed temper-

ature records (Solomon et al. 2007).

Corresponding author address: Benjamin Laken, Instituto de

Astrofısica de Canarias, C/ Vıa Lactea, s/n, 38205 La Laguna,

Tenerife, Spain.

E-mail: blaken@iac.es

4430 J O U R N A L O F C L I M A T E VOLUME 25

DOI: 10.1175/JCLI-D-11-00306.1

� 2012 American Meteorological Society

Despite these facts, notions of a solar–cloud link still

warrant further investigation, particularly as a range of

palaeoclimatic reconstructions have found strong evi-

dence implying the presence of pervasive links between

solar activity cycles and variations in earth’s climate from

a range of distinct sources over periods of up to thou-

sands of years, such as variations in monsoonal activity

recorded by stalagmite growth (Fleitmann et al. 2003);

periodic ice rafting events in the North Atlantic (Bond

et al. 2001); variations in dust layers present in Greenland

ice core samples (Ram and Stolz 1999); and hemispheric-

scale climate anomalies recorded by tree rings (Yamaguchi

et al. 2010). Such connections are difficult to explain via

a conventional understanding of solar–terrestrial con-

nections and may imply the presence of an unknown

mechanism(s) linking small changes in solar activity to

the earth’s climate.

A variety of pathways linking changes in solar activity

to atmospheric variability have been proposed, and de-

tailed descriptions of these pathways are given by Haigh

(1996), Carslaw et al. (2002), and Meehl et al. (2009).

Such connections may provide additional sources of un-

accounted for natural climate variability, which could

have been a significant factor influencing past climate

and may continue to operate as a source of contem-

porary (albeit likely low amplitude) climate variability

(Beer et al. 2000).

In the context of such palaeoclimatic studies, the no-

tion of a cloud-based solar–climate link is particularly

intriguing, as such a connection would have the potential

to amplify the relatively small solar impetus into a cli-

matologically significant effect. This is achieved as the

radiative forcing effects of clouds are large enough, that

even a small change in cloud amount may exert an in-

fluential radiative forcing (Slingo 1990); for example,

an increase of global cloud cover by 1% would alter

earth’s radiative budget by approximately 20.13 W m22

(Ramanathan et al. 1989). An amplifying mechanism

such as this is essential to the idea of a solar–climate

link, as the total energy variance of the sun over an

11-yr solar cycle possesses an amplitude of only

;0.1%; assuming a conservative climate sensitivity of

0.3–1.08C (W m22)21, this value is too small to be of direct

significance to earth’s climate (Kelly and Wigley 1992;

Schlesinger and Ramankutty 1992; Lean and Rind 1998).

Subsequent reanalysis of the initial Svensmark and

Friis-Christensen (1997) GCR–cloud relationship found

that the observed correlation was restricted to low-

altitude (.680 mb) cloud cover only (Marsh and

Svensmark 2000; Palle and Butler 2000) and even this

relationship was found to break down after 1994 (Laut

2003). It was claimed by Marsh and Svensmark (2003) that

this breakdown was the result of a satellite calibration

issue. However, this idea is questionable, as while ad-

justments to the ISCCP dataset occurring following the

addition of new satellite instruments to the network may

conceivably result in a jump in the dataset, it would not

produce a change necessary to obscure a long-term cor-

relation. As a further issue to this notion, it seems that the

long-term trends in the ISCCP dataset may largely result

from satellite-viewing geometry artifacts and not from

physical changes in the atmosphere (Evan et al. 2007).

The accuracy of long-term ISCCP cloud studies is

also limited by errors in the data: estimations of total

relative uncertainties in the radiance calibrations are

around #5% for visible (VIS) and #1% for infrared (IR)

measurements, where absolute calibration uncertainties

are estimated to be around 10% and 2%, respectively

(Brest et al. 1997). Additionally, criticisms regarding

long-term solar–cloud correlation studies have been raised

in relation to the detected relationships being influ-

enced by internal oscillations, such as El Nino (Farrar

2000), and also by long-term artificial errors in the

ISCCP dataset itself (Norris 2000; Palle 2005).

2. Rationale and datasets

In this study we aim to provide an overview of global-

and local-scale correlations between cloud anomalies

and solar activity over the last decade. Cloud data are

drawn from the Moderate Resolution Imaging Spec-

troradiometer (MODIS) project. This dataset provides

several advantages over ISCCP measurements utilized

by similar studies; most significantly, MODIS provides

a greatly increased spectral resolution from which to

determine cloud properties. As MODIS has been active

since 2000, data are available for almost a complete solar

cycle, providing an intriguing opportunity to further in-

vestigate the possibility of long-term relationships be-

tween cloud cover and solar activity in an independent

and modern cloud dataset.

Because of the parallels between this work and simi-

lar long-term analysis carried out with ISCCP data, it is

important to highlight some of the primary differences

between the MODIS and ISCCP datasets: 1) the ISCCP

samples the entire globe 8 times per day from a network

of geostationary and polar-orbiting satellites, whereas

MODIS samples over a 1–2-day period from two in-

struments on board the Earth Observing Systems Aqua

and Terra polar-orbiting platforms. 2) ISCCP uses 11

channels operating between 0.4 and 14.12 mm (although

the actual number of channels viewed by each individual

satellite in the network varies from 2 to 11), whereas

MODIS operates at much higher spectral resolutions,

using 36 channels (between 0.405 and 14.385 mm). 3) To

determine cloud-top pressure ISCCP uses emissivity,

1 JULY 2012 L A K E N E T A L . 4431

whereas MODIS uses a method known as CO2 slicing;

this technique has proven to be more sensitive to certain

cloud types than the ISCCP method (Wylie and Menzel

1999). 4) ISCCP performs both VIS-and-IR-combined

retrievals during daytime and IR-only cloud retrievals

during nighttime and compares these values to adjust

the nighttime (IR only) retrievals, whereas MODIS pro-

vides only a simple daily mean statistic of retrieved cloud

pixels during daytime. 5) Over certain regions MODIS

observes larger cloud amounts than ISCCP; this differ-

ence is most pronounced at low-altitude levels, over land

during daytime (Pincus et al. 2006, 2012). It is important

to note that there is no clear definition of cloud frac-

tion that is applicable across distinct observing systems;

MODIS cloud fractions are comparable to ISCCP, which

in turn have been tested against other measures of cloud

fraction from surface-based observations (Rossow et al.

1993). MODIS cloud fractions are relatively conserva-

tive and frequently reject patchy low-level cloud because

of ambiguous retrievals; this may contribute to a bias in

the dataset.

This investigation also uses both total solar irradiance

(TSI) and GCR flux data: TSI data are taken from the

Physikalisch-Meteorologisches Observatorium Davos

(PMOD) World Radiation Center composite (Frohlich

and Lean 1998; Frohlich 2000), while measurements of

the GCR flux are derived from the Moscow neutron

monitor (55.478N, 37.328E; 200 m; 2.43 GV). In addition,

an index of the El Nino–Southern Oscillation (ENSO) is

also used: the multivariate ENSO index (MEI) (Wolter

and Timlin 1993). The MEI is created from the com-

bined first unrotated principal component of six observed

climate parameters over the topical Pacific, specifically,

sea level pressure, zonal and meridional surface winds,

sea surface temperatures, surface air temperatures, and

total cloud fraction.

3. Methodology

In the following analysis, the mean of the seasonal

cycle is removed (deseasonalized) and the cloud anom-

alies are compared to TSI, GCR, and MEI variations;

all data are utilized at a monthly temporal resolution.

The deseasonalization is performed on the cloud data

by differencing individual monthly averages from the

respective (grouped) monthly average over the entire

dataset.

The cloud data used in this work are drawn from the

combined measurements of the MODIS instruments on

board both the Terra and Aqua platforms. No artificial

changes are introduced into the resulting time series as a

consequence of this combination, and it is noted that the

individual (overlapping) Terra and Aqua cloud fraction

time series show a strong correlation (r 5 0.87) (Fig. 1).

The combination of Terra and Aqua data should provide

an additional level of robustness to the results, as aver-

aging across independent MODIS sensors will reduce

the possibility of instrumental errors influencing the re-

sults. Monthly cloud amount at high-altitude (,440 mb),

midaltitude (440–680 mb), and low-altitude (.680 mb)

levels are calculated from the MOD08 (daily) level 3,

collection 5.1 data (from the ‘‘cloud-top pressure mean’’

and ‘‘cloud fraction combined mean’’ parameters). The

distinction between low-, mid-, and high-altitude levels

is based on ISCCP definitions. It is noted that collec-

tion 5.1 Terra data includes a long-term calibration

drift of currently unknown origins; however, this issue

does not affect the cloud fraction or cloud-top pressure

parameters.

Evaluations of the degree of association between the

datasets are made using correlation coefficients (R).

Cloud data are analyzed at both a globally averaged

geographical resolution and over a local (18 3 18) reso-

lution at varying altitude levels. To accurately determine

the significance of the R values, Monte Carlo (MC) sim-

ulation techniques are employed in the following man-

ner: detrended monthly GCR/TSI data for the entire

data period (i.e., the 10-yr trend is removed) are ran-

domized and correlated against 624-month periods of

detrended monthly cloud data 10 000 times; the critical

significance thresholds are then determined based on

the resulting (normally distributed) R values. An analysis

of autocorrelation within the datasets showed desea-

sonalized MODIS cloud anomalies have only negligible

autocorrelation, while the TSI/GCR and MEI datasets

showed autocorrelations of around 15 months. As this

period is far shorter than the total data period and the

FIG. 1. Agreement between MODIS Terra and Aqua cloud

anomalies. Deseasonalized global MODIS cloud anomalies from

both Terra (solid line) and Aqua (dashed line) instruments over the

period of 2000–11. Data shows an R of 0.87.

4432 J O U R N A L O F C L I M A T E VOLUME 25

MC methodology makes no assumptions regarding

independence, autocorrelation will not influence our es-

timates of statistical significance to a notable degree. For

an examination of the significance of cross correlations

in the deseasonalized and linearly detrended data, lagged

significance thresholds are calculated at globally aver-

aged resolutions. For an analysis of local significance

levels, the MC procedure is performed at the individual

gridcell level (at zero-month lag).

4. Results

The deseasonalized cloud anomalies are presented in

Fig. 2. The total anomaly exhibits virtually no long-term

trend (0.004% month21) and possesses an average stan-

dard deviation over the data period of 1.04% (Fig. 2a).

Cloud anomalies detected at high-altitude levels ex-

hibit a slight decreasing tendency over the data period

of 20.005% month21 and show an average standard de-

viation of 0.90% (Fig. 2b). The midaltitude-level cloud

anomalies show a small increasing tendency (0.002%

month21) and an average standard deviation of 0.78%

(Fig. 2c). The low-level anomalies show an increasing

trend of 0.0069% month21 and an average standard de-

viation of 0.78% (Fig. 2d).

Figure 3 shows both the monthly normalized GCR

flux (%) and the normalized TSI (W m22) outside the

atmosphere. The GCR flux is relatively low (approxi-

mately 26.82%) from 2000 to 2003 during the maximum

of solar cycle 23. It then increases by around 10% from

2003 to 2006, after which time it remains relatively stable

(at around 6.30%), only beginning to decrease in the

latter part of 2009 (Fig. 3a). TSI demonstrates approxi-

mately similar but anticorrelated behavior (the GCR and

TSI data possess an R value of 20.76 over the data pe-

riod): during the first half of the decade, TSI shows a

relatively large monthly standard deviation (during so-

lar maximum) of around 0.46 W m22; however, after this

period this value drops to only 0.08 W m22. The ap-

proximate amplitude of the solar maximum to minimum

TSI change over the period is seen to be 0.90 W m22

(outside of the atmosphere) (Fig. 3b).

An examination of the cross correlations between cloud

at total, high, mid-, and low levels and the TSI/GCR

flux over a 624-month period is presented in Fig. 4. No

strongly significant associations are identified, although

several weakly significant correlations can be seen at a

variety of time lags and inconsistent signs. The only var-

iables found to show a statistically significant (negative)

correlation above the 0.99 critical level were the low

cloud and the TSI flux. However, the TSI data showed

a significant correlation at a 111-month time lag, pre-

cluding the possibility of a causal relationship.

FIG. 2. Anomalous cloud trends. Deseasonalized monthly cloud

anomaly (%) detected by MODIS (Aqua and Terra combined)

between March 2000 and February 2011: (a) total anomaly, (b)

high-level (,440 mb) anomaly, (c) midlevel (440–680 mb) anom-

aly, and (d) low-level (.680 mb) anomaly. Solid line shows mean

anomaly, while dotted lines above and below display one standard

deviation (s) level of the monthly averaged cloud values. Linear

fits are also displayed, along with both the regression equations (Y)

and R values.

1 JULY 2012 L A K E N E T A L . 4433

An examination of locally significant correlations (oc-

curring at zero lag) shows the presence of small and

sporadic regions of largely positive correlation occurring

between total cloud and TSI over areas of the southern

midlatitude ocean regions and also a single relatively

large area located at low-cloud levels over the equatorial

Pacific, coinciding with the region associated with the

Pacific cold tongue (PCT) region (Figs. 5a–d). Although

these areas of positive correlation are found to occur

over high-, mid-, and low-altitude levels, it can also be

observed that negative correlations appear cospatially

(at different altitudes) with many areas of overlying

positive anomalies (while the reverse is also true), for

example, over areas of the North Atlantic. Locally sig-

nificant correlations between GCR variations and cloud

show a range of geographically scattered statistically

significant correlations (Figs. 5e–h). However, the cor-

relations at low-altitude levels tend to be largely neg-

ative, contradicting a suggested positive correlation

between GCR and low-cloud variations (Fig. 5h). Field

significance testing was performed on these results:

only the low-cloud/GCR flux sample was found to be

field significant above the 0.95 significance level (hav-

ing a p value of 0.043).

The MEI shows a decreasing linear trend over the

data period (of 20.004 month21) (Fig. 6a). In relation

to the solar and cloud datasets, the MEI shows (zero lag)

R values of 20.16/20.08 to TSI/GCR, respectively, and

20.06/20.19/20.09/0.28 to total/high/middle/low desea-

sonalized cloud anomalies, (the linear trends are re-

moved from all data prior to tests of cross correlation).

At local (geographical) scales, R values are found to be

both large (up to 60.8) and geographically widespread.

The most apparent features are a large area of positive

(negative) correlation at high (low) levels over the mid-

dle equatorial Pacific and a region of negative (positive)

correlation located largely around Indonesia at high

(low)-altitude levels (Figs. 6b–e).

FIG. 3. GCR and TSI trends. Monthly (a) GCR flux (%) from Moscow neutron monitor and

(b) TSI (W m22) from the PMOD reconstruction. Values are normalized against their re-

spective averages over the entire period (March 2000–February 2011). Solid line shows mean

anomaly, while dotted lines above and below display one s level of monthly averaged values.

Linear fits are also displayed, along with both Y and R values.

4434 J O U R N A L O F C L I M A T E VOLUME 25

The MEI shows a statistically significant (.0.99 two-

tailed level) negative correlation to the GCR flux cen-

tered on month 28 and a secondary significant negative

peak (.0.95 confidence level) occurring around month

116, whereas TSI shows no notable correlation to the

MEI within approximately 620 months surrounding the

zero lag (Fig. 7).

5. Discussion

An analysis of Fig. 1 has shown that total MODIS

cloud measurements exhibit a long-term increase of

0.58% over the past decade, resulting from a decrease in

high- and midlevel cloud cover of 20.31% and an in-

creasing trend in low-cloud levels of 0.89%. If real, this

change implies an increase in shortwave (negative) and

a decrease in longwave (positive) radiative forcing, which

would suggest that over the past decade clouds may have

exerted an enhanced cooling influence on climate of

around 20.075 W m22. However, it is likely that this

observation is partially artificial, as satellite radiometer

measurements are noncloud penetrating. Consequently,

low-level cloud is frequently masked by the occurrence

of overlying cloud. Thus, a physical decline in the

high-/midlevel cloud amount over regions that over-

lap low-level cloud will give the false impression of a

roughly equal magnitude increase in low-level cloud

amount; such occurrences have been noted in the ISCCP

dataset (Palle 2005). This phenomenon is evident in

Fig. 6, which shows cospatial significant correlations of

opposing sign between altitude levels (e.g., the Indo-

nesian region shows both negative correlations at high-

altitude levels and positive correlations at low-altitude

levels). Thus, it is likely that the increase in low-level

cloud (which is more than twice the magnitude of the

reduction in high- and midlevel clouds) is at least par-

tially artificial. It is physically plausible that the linear

reduction detected in the high-level cloud may result

from the shift toward La Nina conditions over the data

period [indicated by the linear decrease of the MEI

(Fig. 6a)].

It is also nontrivial to note that trends detected in the

ISCCP dataset over a comparable time period are found

to be in disagreement with MODIS: from 2000 to 2008

ISCCP D1 IR data shows trends of 20.018% (30 days)21

in low-level clouds and 0.027% (30 days)21 in midlevel

and high-level clouds combined. Although we make no

attempt to test the physical validity or attribution of

these trends, we note that this conflict underscores the

difficulty in accurately measuring global long-term cloud

changes from satellites and suggests that we should limit

the level of confidence that can be placed in the results of

such long-term cloud studies. For a detailed discussion

FIG. 4. Cloud–solar cross correlations for detrended data. Cross-

correlation plots for 624-month time lags for both TSI (solid line)

and GCR (dashed line) against deseasonalized monthly cloud anom-

alies at varying altitude levels: (a) total, (b) high-level (,440 mb),

(c) midlevel (440–680 mb), and (d) low-level (.680 mb). The

linear trends (presented in the Y equations of Figs. 1, 2) are re-

moved from the datasets prior to correlation analysis. Second-

order polynomial fits of MC-simulated significance thresholds are

also displayed over the 624-month lag period at the two-tailed

0.95 (dashed lines) and 0.99 (dotted lines) levels.

1 JULY 2012 L A K E N E T A L . 4435

of relevant ISCCP and MODIS comparisons, see Pincus

et al. (2006, 2012) and references therein.

Although this is the first long-term examination of

MODIS-detected cloud anomalies in relation to solar

activity, MODIS has been previously utilized to test for

the presence of a solar – cloud link by Kristjansson et al.

(2008). This study focused on high-magnitude daily

time-scale decreases in the GCR flux known as Forbush

decrease (FD) events to test for the occurrence of sig-

nificant cloud anomalies over regions theoretically sen-

sitive to the effects of GCR variations; the results of this

work also yielded no compelling evidence of a GCR–

cloud link.

The period of study (2000–10) occurs during the final

phase of solar cycle 23 and the first portion of cycle 24.

The latter has been an abnormal cycle, with an unusually

deep minimum (Nandy et al. 2011). This is clearly evi-

dent in Fig. 2, which shows both TSI and the GCR flux

exhibiting low monthly variability post 2006. Conse-

quently, this may mean that the magnitude (and thus

detectability) of any potential solar–cloud link may also

be comparably low over this period. To be fully confident

FIG. 5. Locally significant solar–cloud correlations. Locally statistically significant (at the 0.95 two-tailed level) R

(at zero lag) plotted between 608N and 608S. (a)–(d) [(e)–(h)] display correlations between cloud anomalies and TSI

[GCR], at all total, high-altitude (,440 mb), midaltitude (440–680 mb), and low-altitude (.680 mb) levels. Critical

significance levels are determined for each pixel by MC simulation methods (see methodology for full description).

Linear trends are removed from the datasets prior to analysis.

4436 J O U R N A L O F C L I M A T E VOLUME 25

that a solar signal is not clearly present in the MODIS

data, this study should be repeated in the future when

data spanning several solar cycles becomes available.

It is plausible that artificial correlations may arise be-

tween TSI and cloud cover, as a result of varying ir-

radiance being received by the satellite instruments.

However, studies observing changes in the TSI flux of

;0.4 W m22 over a period of several days observed no

statistically significant changes in ISCCP cloud cover

(Laken et al. 2011; Laken and Calogovic 2011), imply-

ing that such a situation is likely similarly incapable of

producing artificial cloud changes over annual–decadal

time scales also.

Because of the linked nature of the variations in the

TSI and GCR flux, it is problematic to isolate one var-

iable and claim it as causally linked to atmospheric

changes. However, based on the overall significance of

the correlations achieved, TSI has a higher probability

of being causally related to cloud changes than the GCR

flux: similar conclusions have been drawn by other long-

term correlation analysis studies based on the ISCCP

dataset (Kristjansson et al. 2002, 2004). However, it is

FIG. 6. ENSO index and cloud correlations. (a) Monthly changes in the MEI from 2000 to 2010, with the linear

trend, Y, and R value displayed. Also shown are locally statistically significant correlations to variations in cloud

cover at (b) total, (c) high-altitude (,440 mb), (d) midaltitude (440–680 mb), and (e) low-altitude (.680 mb) levels.

Critical significance levels are determined for each pixel by MC simulation methods (see methodology for full de-

scription). Linear trends are removed from the datasets prior to analysis.

1 JULY 2012 L A K E N E T A L . 4437

clear from the overall poor correlations achieved (Figs.

4, 5) that solar activity is not a dominant factor driving

monthly and annual cloud variability. Indeed, it may

be argued that several of the locally significant corre-

lations identified in Fig. 5 may be reflecting ENSO-

related variability (to which TSI/GCR are found to

demonstrate a weak correspondence); arguments that

long-term solar–ISCCP cloud studies are influenced by

ENSO activity have similarly been made by Farrar (2000).

The results of a cross-correlation analysis between the

MEI and solar anomalies/globally averaged cloud varia-

tions seem to further reinforce such findings: analysis

has shown that GCR anomalies yield a significant an-

ticorrelation to the MEI around month 28; this cor-

relation does not diminish below statistically significant

values until approximately month 23 (Fig. 7). Such a

correlation clearly has no physical basis, and it implies

that the significant correlation detected between GCR

variations and midlevel global cloud anomalies beginning

around month 211 (Fig. 4c) may be due to the chance

correlation between the GCR flux and ENSO variations.

Similarly, the increases in R values around month 112

between TSI and total/high-altitude/midaltitude cloud

anomalies (Figs. 4a–c) are also likely connected to posi-

tive correlations between the aforementioned param-

eters and ENSO variations at this time (Fig. 7).

Despite these largely discouraging findings with re-

lation to the notion of a dominant solar–cloud link, our

local-scale correlation analysis identifies variations in

low-altitude cloud anomalies over the PCT region as

being potentially linked to changes in TSI. Although this

correlation is detected at low-altitude levels, there are

no detected areas of cospatial cloud correlation at

upper-atmospheric levels, indicating that the correla-

tion is likely not an artifact. We, therefore, suggest that

this correlation may represent a plausible region of

TSI-associated cloud variability linked to ENSO, likely

operating via a link between TSI and local sea surface

temperatures; a mechanism for such a link may operate

in a method similar to that proposed by Meehl et al.

(2009).

6. Conclusions

An analysis of the first decade of monthly time-scale

MODIS cloud anomalies has shown that neither varia-

tions in TSI emissions or the GCR flux are dominantly

responsible for cloud variability at global or local (geo-

graphic) scales at any altitude level. Although correla-

tion analysis suggests that some statistically significant

correlations between cloud variability and TSI/GCR

variations are present, further investigation of these

relationships revealed that such associations either

broke down during the data period or were likely con-

nected to internal climate variability and not to solar

activity.

Acknowledgments. The authors kindly thank Jasa

Calogovic (Hvar Observatory), Dr. Dominic Kniveton

(University of Sussex), Dr. Juan Betancort (Instituto de

Astrofısica de Canarias), Dr. Steven Platnick (NASA),

Dr. Robert Pincus (NOAA), Dr. Robert Wood (Uni-

versity of Washington), and two anonymous referees for

their constructive comments. The MODIS data were ob-

tained from the NASA website (http://ladsweb.nascom.

nasa.gov), while the cosmic ray data were obtained from

the Solar Terrestrial’s physics division of IZMIRAN

(http://helios.izmiran.rssi.ru). The authors acknowledge

the PMOD dataset (version d41_62_1102): PMOD/WRC

of Davos, Switzerland, which also comprises unpublished

data from the VIRGO experiment on the ESA/NASA

mission SoHO. MEI NOAA/OAR/ESRL/PSD data

were obtained from online (at www.esrl.noaa.gov/

psd//people/klaus.wolter/MEI/).

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