Forbush decreases, solar irradiance variations, and anomalouscloud changes

Benjamin Laken,1 Dominic Kniveton,2 and Arnold Wolfendale3

Received 17 August 2010; revised 26 February 2011; accepted 7 March 2011; published 6 May 2011.

[1] Changes in the galactic cosmic ray (GCR) flux due to variations in solar activity mayprovide an indirect connection between the Sun’s and the Earth’s climates. Epochsuperpositional (composite) analyses of high‐magnitude GCR fluctuations, known asForbush decrease (FD) events, have been widely used to test this hypothesis, with variedresults. This work provides new information regarding the interpretation of this approach,suggesting that FD events do not isolate the impacts of GCR variations from those ofsolar irradiance changes. On average, irradiance changes of ∼0.4 W m−2 outside theatmosphere occur around 2 days in advance of FD‐associated GCR decreases. Using this2 day gap to separate the effects of irradiance variations from GCR variations on cloudcover, we demonstrate small, but statistically significant, anomalous cloud changesoccurring only over areas of the Antarctic plateau in association with the irradiancechanges, which previous workers had attributed to GCR variations. Further analysis of thesample shows that these cloud anomalies occurred primarily during polar darkness,precluding the possibility of a causal link to a direct total solar irradiance effect. This worksuggests that previous FD‐based studies may have ineffectively isolated the impacts ofGCR variations on the Earth’s atmosphere.

Citation: Laken, B., D. Kniveton, and A. Wolfendale (2011), Forbush decreases, solar irradiance variations, and anomalouscloud changes, J. Geophys. Res., 116, D09201, doi:10.1029/2010JD014900.

1. Introduction

[2] The occurrence of interplanetary magnetohydrody-namic disturbances resulting from variations in solar activityproduces sudden decreases in the observed galactic cosmicray (GCR) flux known as Forbush decreases (FDs). Over thepast 20 years, these events have been used by numerousworkers [e.g., Pudovkin and Veretenenko, 1995; Todd andKniveton, 2001, 2004; Svensmark et al., 2009; Laken et al.,2009] to test notions of a link between the GCR flux andEarth’s climate via a modulation of cloud cover as proposedby Svensmark and Friis‐Christensen [1997]. FD eventsprovide a seemingly ideal opportunity to test the validity ofthis hypothesis for several reasons: they are relatively abruptfeatures, during these events the GCR flux varies byapproximately the same amount as experienced over an 11 yearsolar cycle, and compositing these events may provide ameans to isolate the effects of GCRs from alternative solaractivity parameters such as solar irradiance (SI) changes,which may influence the atmosphere. FD events also isolatecloud changes from the effects of multiyear fluctuationssuch as those resulting from El Niño or volcanic eruptions.However, the use of FD events as a sampling basis has not

provided statistically clear evidence of a link between GCRsand cloud cover changes. Instead, FD‐based studies haveproduced a range of conflicting results: some studies reportthat no cloud changes are observed over regions that havebeen suggested to be sensitive to variations in the GCR flux[Pallé and Butler, 2001; Lam and Rodger, 2002;Kristjánssonet al., 2008; Laken et al., 2009], while other studies haveidentified decreases in localized cloud cover over high‐latitude regions [Pudovkin and Veretenenko, 1995; Solovyevand Kozlov, 2009]. In particular, several studies have foundevidence of anomalous cloud changes over high‐altituderegions of the Antarctic plateau [Todd and Kniveton, 2001,2004; Laken and Kniveton, 2010]. Additionally, increases(as distinct from decreases) in cloud cover associated withFD events have even been reported over limited areas ofChina and the Antarctic [Wang et al., 2006; Troshichev et al.,2008].[3] There are several possible reasons for the failure of

FD‐based studies to detect coherent and statistically robustcloud changes: (1) no genuine relationship actually existsbetween GCRs and cloud cover; (2) there is, in fact, a rela-tionship, but the small sample sizes inherent to FD‐basedstudies result in a low signal‐to‐noise ratio; (3) the accuracyof satellite‐based cloud retrievals over high‐latitude regionsis limited, which may complicate detection of a GCR‐cloudrelationship [Todd and Kniveton, 2004; Laken and Kniveton,2010]; (4) a GCR‐cloud relationship may be real; however,it may be weak compared to other internal factors thatinfluence cloud properties (such as dynamical factors); and

1Instituto de Astrofísica de Canarias, San Cristóbal de la Laguna, Spain.2Department of Geography, University of Sussex, Brighton, UK.3Department of Physics, Durham University, Durham, UK.

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(5) the response of clouds to GCR variations may bestrongly governed by the cloud conditions that exist prior toGCR changes [Laken et al., 2010]. In addition to theseproblems, we suggest a further consideration: the assump-tion that FD‐based samples may provide a method of dis-tinguishing the influences of GCR and SI changes mayrequire reexamination because of the connection betweenFD events and solar emissions. A dramatic example of thisis apparent from examination of the extreme solar flareevents known as the “Halloween storms” (which occurredbetween 28 October 2003 and 4 November 2003). Duringthis time, a strong SI response was detected together withchanges in all of the other solar emissions (such as UVradiation, X‐rays, and radio waves) [Woods et al., 2004].This observation implies that GCR changes during FDevents may not be isolated from SI variations, and these datasuggest that an examination of SI changes during the rangeof observed FD events is required.[4] It is necessary to discuss the significance of SI chan-

ges; the radiation from the Sun comes from regions rangingfrom the photosphere to the corona and is emitted at allfrequencies, from radio to gamma rays. Total solar irradi-ance (TSI) emissions are mainly in the visible and near‐infrared regions. Negative TSI fluctuations of ∼6 W m−2

occur approximately once every 7 years (on the basis of

values from the Solar Radiation and Climate Experiment(SORCE) [Rottman et al., 2006] measured between 2003and 2010, with about ten of those excursions exhibitingfluctuations of >1 W m−2 and having a full width at halfmaximum (FWHM) change of approximately 6 days). Ideally,this analysis should concentrate on the TSI itself, but spec-trally resolved measurements of this parameter did not startuntil 2003 (SORCE). However, a composite of TSI mea-surements is available from the Active Cavity RadiometerIrradiance Monitor (ACRIM) reconstructions, which extendto 1978 [Willson and Mordvinov, 2003]; these data have beenused in the main analysis. A subsidiary analysis has also beenmade using the 10.7 cm (2800 MHz) radio signal (hereafterreferred to as RI), which has been measured almost contin-uously since 1947. The RI is generated by free‐free gyro-resonance emission from sunspot structures and is positivelycorrelated with UV emissions [Tobiska, 2001]. Inspection ofTSI and RI data from 1978 to 2010 shows a good (negative)correlation over the 10 strongest TSI decreases, confirmingthe usefulness of RI as a proxy.[5] The reason for analyzing both TSI and RI values (EUV

proxy [Rich et al., 2003]) is that both could have a relation-ship to atmospheric parameters (such as cloud cover): for thetroposphere there is direct cooling from reduced energyinput, while in the case of RI there is UV heating of the

Figure 1. List of 123 adjusted Forbush decrease events. Original onset dates are taken from severalsources: NOAA’s online database (available at http://www.ngdc.noaa.gov/stp/solar/cosmic.html#FDs),Todd and Kniveton [2001, 2004], and Kristjánsson et al. [2008]. Original FD onset events wereadjusted so as to align day 0 with the maximal GCR decrease in an identical approach to that of Laken andKniveton [2010]. The magnitude of the mean FD decrease is 4.8% of the mean counts. The FD dates arerestricted to events occurring from 1978 onward because of the availability of TSI measurements.

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stratosphere, which may be dynamically linked to tropo-spheric variability [Haigh, 1996; Lockwood et al., 2010].The time delay for the UV heating to be transmitted to thetroposphere is probably tens of days [Larkin et al., 2000];however, there are stratospheric effects which may act morerapidly. For example, it has been proposed that the UVinitiation of stratospheric aerosols may affect the atmospherictransmission of TSI within a time period of 10 h, thereby

influencing the amount of radiation received by the Earth’ssurface [Kudryavtsev and Jungner, 2005; Kudryavtsev,2007].

2. Materials and Methods

[6] A composite (epoch superpositional) methodology isused on a sample of 123 FD onset events occurring from

Figure 2. Absolute GCR and SI variations during FD events. (a) Mean GCR flux and (b) TSI variationsoccurring over a composite of 123 FD events (1978–2005). Day 0 indicates the maximal GCR decrease dateassociated with FD events (vertical dashed line). Dotted line around mean GCR and TSI values denotes the0.95 level confidence intervals. GCR data are taken from the Colorado Climax neutronmonitor (adjusted forbarometric pressure variations). TSI data are taken from the ACRIM reconstruction, which has been cor-rected for degradation and reconciled to 1 AU. Values of the composite have been normalized againstday 0 values for display purposes.

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1978 to 2006, where an FD event is explicitly defined as aGCR decrease at the Earth’s surface of >3%, recorded bythe neutron monitor located at Mount Washington, NewHampshire (44.3°N, 288.7°E) [Todd and Kniveton, 2004].These 123 onset dates are adjusted to the maximal GCRdecrease date in the same manner as Laken and Kniveton[2010], whereby each key date (FD onset event) is rea-ligned to reflect the date of maximal GCR decrease detectedat the Climax neutron monitor in Colorado. This approach isrequired, as the GCR minimum of an individual FD eventmay occur up to several days after the initial onset date[Troshichev et al., 2008]. As a result, realigning the datesprovides a composite which isolates GCR decreases to a highdegree, making the sample ideally suited for an examinationrelated to the GCR flux.[7] The original FD onset dates are drawn from several

sources: NOAA’s National Geophysical Data Center onlinearchives (http://www.ngdc.noaa.gov/stp/solar/cosmic.html#FDs),Todd and Kniveton [2001, 2004], and Kristjánsson et al.[2008]. The full list of realigned dates is presented in Figure 1.[8] For reference, the biggest FD reduction (on 14 July

1982) amounted to 20.3%. To set the scale of the magnitudeof the FD events, the numbers of observations in intervals of5% GCR decrease ranges follow: 64 in <5%, 35 in 5%–10%,3 in 10%–15%, and 2 in 15%–20% (missing Climax neutronmonitor data account for the missing 19 events). The TSI dataare taken from the ACRIM reconstruction, while adjustedRI data are recorded from sites at Ottawa and Penticton(Canada). The composite data are presented for a ±10 dayperiod around the key date of maximal GCR decrease.[9] An analysis of anomalous total cloud changes occur-

ring over the Earth is also presented. These data are obtainedfrom the International Satellite Cloud Climatology Project(ISCCP) D1 data set (retrieved from the visible light spec-trometer, network interface, and IR channels) [Rossow et al.,1996]. The ISCCP data are created from intercalibrated radi-ancemeasurements recorded by a fleet of satellites and provide

global coverage of cloud and other atmospheric parameters onan equal area grid of 280 × 280 km2, at a 3 h temporal reso-lution from 1983 to 2008. There are several limitations asso-ciated with the ISCCP data, such as the presence of artificialcloud changes resulting from satellite drifts, intercalibrationerrors, and view angle limitations (because of the top‐downviewing perspective of satellites, high‐level clouds mayobscure those at relatively lower atmospheric levels). For adetailed review of ISCCP limitations, see Pallé [2005] andEvan et al. [2007]. Cloud changes during the compositeperiod will be calculated as a differential cloud change(DCC), where the calculated cloud change on each day of thecomposite period is equal to the average cloud cover of thatday subtracted from the average cloud cover of a 3 day periodbeginning 5 days prior. The 2 day gap between each day andits averaging period is necessary to account for temporalautocorrelation in the cloud data set. The resulting DCC valuemay be considered to be the change in percentage of cloudcover over a 4 day period.[10] The statistical significance of the DCC variations

presented is evaluated through the use of Monte Carlomethods, whereby 1000 randomly generated samples of equaldimension to those being tested are generated. The distribu-tion of the resulting DCC values is then used to establish thecritical threshold values for 0.95 (two‐tailed) statisticallysignificant changes; this approach is applied to test the sta-tistical significance of DCC variations at a global scale, at a10° latitudinally averaged scale, and for individual ISCCPpixels.

3. Results

[11] The GCR and TSI changes detected over the FDcomposite are presented in Figure 2. These data show thatthe TSI decrease begins after day −7 and reaches a maximaldecrease of ∼0.41 W m−2 on day −1, while the GCRdecrease begins around day −2 and reaches its maximal

Figure 3. RI variations over the composite period. Values are displayed in flux units (fu) where 1 fu =10−22Wm−2 Hz−1. Values of the composite have been normalized against day 0 values for display purposes.

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decrease (of ∼4.53%) on day 0. Following the maximaldeviations, both GCR and TSI values recover to undisturbedvalues over a period of approximately 1 week at a rate of0.31% d−1 and 0.03 W m−2 d−1, respectively.[12] Inspection of Figure 2 shows that at day −2 the drop

in TSI is at 94.1% of its maximum amplitude (which itreaches at day −1). The FWHM for the GCR decrease is5.0 days, and that of the TSI is similar at 6.2 days. TheTSI flux begins to decrease at day −6, whereas the GCR fluxremains constant until around day −2. The equivalent plotfor RI (Figure 3) shows an increase from day −10 to a broad

4 day maximum beginning around day −3, with a FWHM of10.7 days. Interestingly, the TSI values recover more rapidlythan does the RI. Scatterplots of the relationships betweenGCR variations and TSI and RI are shown in Figure 4.TSI and GCRs show a positive correlation of R = 0.321(Figure 4a), while RI and GCRs show a negative correlationof R = −0.272 (Figure 4b). Although there is a wide degreeof scatter present in the TSI‐GCR and RI‐GCR relationships,it appears that, in general, during the largest FD events thecorrelation is larger (e.g., for events with GCR decreasesgreater than −6%, R = 0.41 and R = −0.52, for TSI and RI,

Figure 4. TSI and RI relationships to GCR flux. Relationship between (a) TSI and GCR flux and (b) RIand GCR flux. Linear regression (dashed line) is displayed, along with the regression equation (y), the0.95 level correlation coefficient value (R), and the two‐tailed probability level (p) (n = 123). GCR valuesare calculated as the difference between day −3 and day 0 counts, while TSI values are calculated as thedifference between day −8 and day −1 values. GCR data are taken from the Colorado Climax neutronmonitor (adjusted for barometric pressure variations). TSI values are taken from the ACRIM reconstruc-tions. RI values are taken from the adjusted 10.7 cm (2800 MHz) radio flux recorded at sites in Ottawaand Penticton (Canada).

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respectively (30 of 34 observations)). This suggests thatlarger FD events may be more prone to associated strongirradiance changes. This is further illustrated in Figure 5,which presents the linear relationship between the GCR fluxand TSI and RI values for events grouped by themagnitude ofGCR decrease. However, it should be restated that the num-ber of events at high‐magnitude GCR decreases is limited.

[13] From the GCR‐TSI changes presented in Figure 2,it can be shown that by day −2 the average TSI values haveundergone ∼94% of its maximum decrease, whereas GCRchanges at this point are still within the undisturbed errorrange. This is due to the different speeds at which light fromthe Sun and the solar wind reach the Earth. On the basis ofthis time lag, we can effectively isolate the influence of TSI

Figure 5. TSI and RI relationships to the GCR flux grouped by magnitude. The relationships between(a) TSI and GCR flux and (b) RI and GCR flux are grouped by the magnitude of the GCR deviations(at intervals of 5%). The linear regression (dashed line) is displayed, along with the regression equation (y),the correlation coefficient (R), and the two‐tailed probability level (p). Values are calculated in an iden-tical method to the data displayed in Figure 4. The error range around data points indicates 1 standarddeviation in each direction.

Figure 6. ISCCP cloud analysis. Analysis of ISCCP D1 total cloud amount (retrieved from visible light spectrometer IRand network interface channels) during the composite period is presented; sample size is limited by the ISCCP monitoringperiod (1983–2008), narrowing the observations to 79 events. (a) Equal area–adjusted global mean cloud amount (solidline) with standard error of the mean (SEM) values (dashed lines). (b) Vertical profile of 10° latitudinally averaged cloudanomalies during day −2 of the composite; dashed line indicates region of field significant anomalies. (c) Locally significantcloud anomaly on day −2 at upper ISCCP pressure level (30–180 mbar); nonsignificant anomalies are not displayed.In Figures 6b and 6c, the statistical significance at the 0.95 (two‐tailed) level is determined by Monte Carlo simulationsof 1000 randomized DCC distributions matching the dimensions of the original samples.

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Figure 6

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emissions from GCRs and analyze associated cloud coverchanges. The results of our cloud analysis are presented inFigure 6. Figure 6a shows globally averaged (equal area–adjusted) total cloud cover at 30–180 mbar. Although nostatistically significant changes are observed, there appearsto be a decrease in cloud amount of ∼0.3% centered aroundday −2; however, a change of roughly equal (nonsignificant)proportions also occurs several days later, centered aroundday +6. An evaluation of 10° latitudinally averaged anom-alous cloud changes occurring on day −2 indicates that anegative field‐significant anomaly is present at high pres-sure levels (30–180 mbar) over high southern latitudes(Figure 6b). These cloud decreases (of approximately−10 DCC) are found to be locally significant over wideregions of the Antarctic plateau; at this pressure level over theAntarctic the detected cloud decreases are located in thestratosphere and may therefore likely concern polar strato-spheric clouds (PSCs). In addition, small scattered anomaliesof both positive and negative sign appear to be widely dis-tributed across the globe; however, these anomalies are likelynoise (Figure 6c). A further examination of cloud changesoccurring between 30 and 180 mbar over the Antarctic region(60°S–90°S) shows a rapid cloud decrease of around 0.7%occurring from day −5 to −2; these cloud changes show acorrelation of 0.77, −0.72, and 0.45 to TSI, RI, and GCR,respectively, over the composite period (Figure 7).[14] To determine if the detected anomalous cloud changes

are causally linked to TSI emissions, it is useful to furtherdecompose the composite sample into events occurringduring polar darkness and daylight over Antarctica. Events

occurring during and immediately surrounding solsticemonthswere selected from the composite as subsamples: the resulting30–180 mbar cloud anomalies detected over Antarctica arepresented in Figure 8 and show that the decreases occurduring periods of polar darkness but not in daylight (this isprecisely the time of year when PSCs are most likely to form

Figure 7. Antarctic stratospheric cloud changes. Timeseries of 30–180 mbar cloud amount (%) from ISCCP D1over the Antarctic region (60°S–90°S) for a ±10 day periodaround the key date of maximal GCR decrease. Dashed linesshow SEM values.

Figure 8. Antarctic stratospheric cloud anomaly, day andnight. Anomalous (DCC) cloud changes occurring duringday −2 of the composite, between 30 and 180 mbar overAntarctica, for events during the months of (a) November,December, and January (NDJ) (n = 16) and (b) June, July,and August (JJA) (n = 19).

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at these altitudes). Consequently, we may conclude thatthese cloud changes likely bear no causal relationship tothe cotemporal TSI variations.

4. Discussion and Conclusions

[15] These results demonstrate that during FD events, bothsolar irradiance and the GCR flux undergo connected changes.Variations in irradiance precede those of the GCR flux byapproximately 2 days because of the difference between thespeed at which light and the solar wind travel from the Sun tothe Earth. The TSI decrease observed outside of the atmo-sphere is around 0.4 W m−2 on average (which translates to aglobally averaged radiative forcing of ∼0.07 W m−2). Thisfinding undermines the notion that FD events provide asampling basis from which to test the effects of GCRs onEarth’s atmosphere distinct from solar irradiance variations.We speculate that this may have significant implicationsfor all FD‐related studies, particularly those that have focusedon high‐magnitude FD events, as the degree of correlationbetween FD events and TSI and RI values appears to increasewith magnitude. This point may be particularly significantas many workers have chosen to filter FD events by theirmagnitude, reasoning that larger GCR decreases may moreeffectively isolate periods of significant GCR‐related cloudchanges [e.g., Kristjánsson et al., 2008; Laken et al., 2009;Svensmark et al., 2009; Čalogović et al., 2010].[16] It is possible that the location of cloud changes over

land and ocean regions may relate to a particular solar forcingmechanism: Udelhofen and Cess [2001] have shown evi-dence that suggests that a positive correlation between TSIemissions and cloud cover is observed over North Americafrom 1900 to 1987, while small but statistically significant(positive) correlations between the GCR flux and cloud coverhave been identified over some ocean areas [Kristjánssonet al., 2008]. An analysis of anomalous cloud changes occur-ring during the TSI decreases of day −2 (Figure 6b) showedthat cloud anomalies of positive and negative sign areubiquitous over both the land‐dominated Northern Hemi-sphere and the ocean‐dominated Southern Hemisphere,implying that no clear widespread solar irradiance forcing isevident in the cloud data despite the slight decrease in globalcloud amount (Figure 6a). A spatially limited, statisticallysignificant cloud decrease was detected at the stratosphericlevel over regions of the Antarctic plateau. It is important tonote that there is a high level of uncertainty associated withcloud retrievals over such regions, and consequently, theconfidence placed in the validity of the detected cloud signalis limited. However, these findings are directly comparable toanomalies first highlighted by Todd and Kniveton [2001,2004] and expanded upon by Laken and Kniveton [2010](hereafter collectively referred to as TKL). Our results indi-cate that these cloud anomalies are neither causally associatedwith the GCR flux (as they occur prior to the GCR decrease),nor are they causally associated with TSI variations (as theyoccur during polar darkness). It is possible that a solar‐GCRconnected cloud response may possess a time lag of up to afew days, although no delayed response is readily apparentwithin the approximately 10 day period presented here.[17] In addition, these results resolve a discrepancy between

the findings of TKL and Troshichev et al. [2008]: the latteridentified cloud changes of positive sign during the key dates

of (GCR minimum) adjusted FD events over areas ofAntarctica from surface‐based cloud measurements. Whilethis finding is confirmed here (Figure 7), the cloud changesare preceded by a much larger decrease and appear to beunrelated to GCR variability.[18] Finally, we stress that these results should not be

interpreted to suggest that there is no possibility of GCR‐connected atmospheric changes at all; indeed, recent evidenceindicates that a link between GCRs and tropospheric cloudcover may operate under certain conditions, contributing toatmospheric variability [Laken et al., 2010]. Instead, theresults presented here simply show that because of the effectof FD‐coincident solar irradiance changes, caution is neededto extract a meaningful signal from FD‐based analyses.

[19] Acknowledgments. The authors would like to thank Enric Pallé(Instituto de Astrofísica de Canarias), Jon Egil Kristjánsson (University ofOslo), Jasa Čalogović (Hvar Observatory), and two anonymous reviewersfor their comments. We also acknowledge the National Space Science DataCenter OMNIWeb database and the National Oceanic and AtmosphericAdministration’s National Geophysical Data Center SPIDR project foraccess to relevant data sets. ACRIM data were obtained from http://www.acrim.com/Data%20Products.htm. The ISCCP D1 data are availablefrom the ISCCP Web site at http://isccp.giss.nasa.gov/, maintained by theISCCP research group at the NASA Goddard Institute for Space Studies.The authors also thank the John C. Taylor Charitable Foundation for support.

ReferencesČalogović, J., C. Albert, F. Arnold, J. Beer, L. Desorgher, and E. Flueckiger(2010), Sudden cosmic ray decreases: No change of global cloud cover,Geophys. Res. Lett., 37, L03802, doi:10.1029/2009GL041327.

Evan, A. T., A. K. Heidinger, and D. J. Vimont (2007), Arguments againsta physical long‐term trend in global ISCCP cloud amounts, Geophys.Res. Lett., 34, L04701, doi:10.1029/2006GL028083.

Haigh, J. D. (1996), The impact of solar variability on climate, Science,272(5264), 981–984, doi:10.1126/science.272.5264.981.

Kristjánsson, J. E., C. W. Stjern, F. Stordal, A. M. Fjæraa, G. Myhre,and K. Jónasson (2008), Cosmic rays, cloud condensation nuclei andclouds—A reassessment using MODIS data, Atmos. Chem. Phys., 8(24),7373–7387, doi:10.5194/acp-8-7373-2008.

Kudryavtsev, I. V. (2007), Variations in the optical and IR transparency ofthe Earth’s atmosphere under the action of cosmic rays and change in thethermodynamic parameters of the atmosphere, Bull. Russ. Acad. Sci.Phys., 71, 1021–1023, doi:10.3103/S1062873807070386.

Kudryavtsev, I. V., and H. Jungner (2005), A possible mechanism of theeffect of cosmic rays on the formation of cloudiness at low altitudes,Geomagn. Aeron., 45(5), 641–648.

Laken, B. A., and D. R. Kniveton (2010), Forbush decreases and Antarcticcloud anomalies in the upper troposphere, J. Atmos. Sol. Terr. Phys.,73(2–3), 371–376, doi:10.1016/j.jastp.2010.03.008.

Laken, B., A. Wolfendale, and D. Kniveton (2009), Cosmic ray decreasesand changes in the liquid water cloud fraction over the oceans, Geophys.Res. Lett., 36, L23803, doi:10.1029/2009GL040961.

Laken, B., D. Kniveton, and M. Frogley (2010), Cosmic rays linked to rapidmid‐latitude cloud changes, Atmos. Chem. Phys., 10, 10,941–10,948,doi:10.5194/acp-10-10941-2010.

Lam, M. M., and A. S. Rodger (2002), The effect of Forbush decreases ontropospheric parameters over South Pole, J. Atmos. Sol. Terr. Phys., 64(1),41–45, doi:10.1016/S1364-6826(01)00092-X.

Larkin, A., J. D. Haigh, and S. Djavidnia (2000), The effect of solar UVirradiance variations on the Earth’s atmosphere, Space Sci. Rev., 94(1–2),199–214, doi:10.1023/A:1026771307057.

Lockwood, M., C. Bell, T. Woollings, R. G. Harrison, L. J. Gray, andJ. D. Haigh (2010), Top‐down solar modulation of climate: Evidence forcentennial‐scale change, Environ. Res. Lett., 5, 034008, doi:10.1088/1748-9326/5/3/034008.

Pallé, E. (2005), Possible satellite perspective effects on the reported corre-lations between solar activity and clouds, Geophys. Res. Lett., 32,L03802, doi:10.1029/2004GL021167.

Pallé, E., and C. J. Butler (2001), Sunshine records from Ireland: Cloud fac-tors and possible links to solar activity and cosmic rays, Int. J. Climatol.,21(6), 709–729, doi:10.1002/joc.657.

LAKEN ET AL.: SOLAR IRRADIANCE AND FORBUSH DECREASES D09201D09201

9 of 10

Pudovkin, M. I., and S. V. Veretenenko (1995), Cloudiness decreases asso-ciated with Forbush‐decreases of galactic cosmic rays, J. Atmos. Sol.Terr. Phys., 57(11), 1349–1355, doi:10.1016/0021-9169(94)00109-2.

Rich, F. J., P. J. Sultan, and W. J. Burke (2003), The 27‐day variations ofplasma densities and temperatures in the topside ionosphere, J. Geophys.Res., 108(A7), 1297, doi:10.1029/2002JA009731.

Rossow, W. B., A. W. Walker, D. E. Beuscel, and M. D. Roiter (1996),International Satellite Cloud Climatology Project (ISCCP) documentationof new cloud datasets, Tech. Doc. 737, 115 pp., World Meteorol. Organ.,Geneva, Switzerland.

Rottman, G. J., T. N. Woods, and W. McClintock (2006), SORCE solarUV irradiance results, Adv. Space Res., 37(2), 201–208, doi:10.1016/j.asr.2005.02.072.

Solovyev, V. S., and V. I. Kozlov (2009), Influence of cosmic ray varia-tions on cloudiness distribution over northern Asia, Russ. Phys. J., 52(2),127–131, doi:10.1007/s11182-009-9209-4.

Svensmark, H., and E. Friis‐Christensen (1997), Variation of cosmic rayflux and global cloud coverage—A missing link in solar‐climate relation-ships, J. Atmos. Sol. Terr. Phys., 59(11), 1225–1232, doi:10.1016/S1364-6826(97)00001-1.

Svensmark, H., T. Bondo, and J. Svensmark (2009), Cosmic ray decreasesaffect atmospheric aerosols and clouds, Geophys. Res. Lett., 36, L15101,doi:10.1029/2009GL038429.

Tobiska, W. K. (2001), Validating the solar EUV proxy, E10.7, J. Geophys.Res., 106(A12), 29,969–29,978, doi:10.1029/2000JA000210.

Todd, M. C., and D. R. Kniveton (2001), Changes in cloud cover asso-ciated with Forbush decreases of galactic cosmic rays, J. Geophys. Res.,106(D23), 32,031–32,041, doi:10.1029/2001JD000405.

Todd, M. C., and D. R. Kniveton (2004), Short‐term variability in satellite‐derived cloud cover and galactic cosmic rays: An update, J. Atmos. Sol.Terr. Phys., 66(13–14), 1205–1211, doi:10.1016/j.jastp.2004.05.002.

Troshichev, O., V. Vovk, and L. Egorova (2008), IMF‐associated cloudinessabove near‐pole stationVostok: Impact onwind regime inwinter Antarctica,J. Atmos. Sol. Terr. Phys., 70(10), 1289–1300, doi:10.1016/j.jastp.2008.04.003.

Udelhofen, P. M., and R. D. Cess (2001), Cloud cover variations over theUnited States: An influence of cosmic rays or solar variability?, Geophys.Res. Lett., 28(13), 2617–2620, doi:10.1029/2000GL012659.

Wang, M.‐J., L.‐P. He, and H.‐Y. Jia (2006), Cloudiness variationobserved at Yangbajing during Forbush decrease of galactic cosmic rays,High Energy Phys. Nucl. Phys., 30(1), 75–78.

Willson, R. C., and A. V. Mordvinov (2003), Secular total solar irradiancetrend during solar cycles 21–23, Geophys. Res. Lett., 30(5), 1199,doi:10.1029/2002GL016038.

Woods, T. N., F. G. Eparvier, J. Fontenla, J. Harder, G. Kopp,W. E. McClintock, G. Rottman, B. Smiley, and M. Snow (2004), Solarirradiance variability during the October 2003 solar storm period,Geophys. Res. Lett., 31, L10802, doi:10.1029/2004GL019571.

D. Kniveton, Department of Geography, University of Sussex, Falmer,Brighton BN1 9QJ, UK.B. Laken, Instituto de Astrofísica de Canarias, E‐38205 San Cristóbal de

la Laguna, Tenerife, Spain. (blaken@iac.es)A. Wolfendale, Department of Physics, Durham University, South Road,

Durham DH1 3LE, UK.

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