Solar dimming over the tropical urban region of Hyderabad, India: Effect of increased cloudiness and increased anthropogenic aerosols

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1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 115,, doi: /2009jd013694, 2010 Solar dimming over the tropical urban region of Hyderabad, India: Effect of increased cloudiness and increased anthropogenic aerosols K. V. S. Badarinath, 1 Anu Rani Sharma, 1 D. G. Kaskaoutis, 2 Shailesh Kumar Kharol, 1 and H. D. Kambezidis 2 Received 13 December 2009; revised 29 April 2010; accepted 22 July 2010; published 11 November [1] The present work examines the multidecadal changes in the net downward shortwave radiation (NDSWR) over the urban region of Hyderabad, India, during the period The trends in NDSWR and in cloud optical depth (COD) are analyzed over the region using the Modern Era Retrospective Analysis for Research and Applications (MERRA 2D), which is a newly available data set. The NDSWR exhibits a significant reduction of W m 2 for the period ; however, it declined at a slower rate (0.468 W m 2 ) during the period This reduction translates to 1.56 W m 2 yr 1 in the first period and to 0.04 W m 2 yr 1 in the second. The large difference in the NDSWR trends between the two periods is mainly attributed to the dramatic increase in high COD and in aerosol optical depth (AOD) during the last decade. The total COD over the region suggests an increase of 6.85% during the study period, with the larger increase to be depicted in the high level clouds (70.86%). Furthermore, retrievals of AOD 550 from Microtops II Sun photometer and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) show a significant increase of 34% and 20% during the period over Hyderabad, respectively. It is demonstrated that aerosols and clouds contribute to the remarkable reduction in NDSWR over Hyderabad in the recent years, thus resulting in a continuous solar dimming over the area. The main role in the decline of NDSWR has been the increase in COD of the high clouds, mainly in monsoon and premonsoon. On the other hand, the dramatic increase in the AOD 550 plays an important role in the decrease of NDSWR, mainly in winter. Furthermore, the indirect aerosol effect can modify the cloud formation, albedo, and lifetime, also playing a key role in the aerosol cloud radiation interactions. Citation: Badarinath, K. V. S., A. R. Sharma, D. G. Kaskaoutis, S. K. Kharol, and H. D. Kambezidis (2010), Solar dimming over the tropical urban region of Hyderabad, India: Effect of increased cloudiness and increased anthropogenic aerosols, J. Geophys. Res., 115,, doi: /2009jd Introduction [2] Solar radiation incident at the Earth s surface is the primary energy source, which largely determines the life parameters on the planet as it plays a significant role in the hydrological cycle, the climatic conditions, the surface energy balance, the snow and glaciers melting, the plant photosynthesis and the related carbon uptake, as well as the diurnal and seasonal temperature variations [Wild et al., 2004]. Solar radiation has also practical implications in 1 Atmospheric Science Section, Oceanography Division, National Remote Sensing Centre, Department of Space, Government of India, Hyderabad, India. 2 Atmospheric Research Team, Institute for Environmental Research and Sustainable Development, National Observatory of Athens, Athens, Greece. Copyright 2010 by the American Geophysical Union /10/2009JD solar energy technologies, such as photovoltaic systems and agricultural productivity. Several studies (included in the review by Wild [2009]) have proved that the amount of solar radiation on the ground is not stable over the years but undergoes significant decadal variations. Therefore, any variations in the solar radiation reaching the Earth s surface directly or indirectly affect all the above processes, thus resulting in climate change [Wild, 2009]. These variations can be of natural origin, induced by major volcanic eruptions [Ohvril et al., 2009], changes in the Earth orbital parameters and in cloud cover [Norris and Wild, 2007, 2009]. On the other hand, the anthropogenic emissions lead to alteration of solar radiation in the polluted atmospheres [e.g., Rappengluck et al., 2003; Badarinath et al., 2007a]. To this respect, the last report of the Intergovernmental Panel on Climate Change [Intergovernmental Panel on Climate Change, 2007] highlights the multidecadal 1of18

2 variations in solar radiation as well as their climatological consequences. [3] A number of studies conducted over various worldwide locations have suggested significant variations in solar radiation intensity considered as solar dimming and brightening phenomenon [e.g., Liepert, 2002; Wild et al., 2004, 2005; Norris and Wild, 2007; Ohmura, 2009; Zerefos et al., 2009]. The majority of these studies have been included in the review study by Wild [2009], and nearly all conclude to a decrease in solar radiation during the period (solar dimming) and a recovery (solar brightening) at some locations in the 2000s. During 1980s, the reduction in total solar radiation under cloudless conditions was established by several studies [Stanhill and Cohen, 2001; Stanhill and Moreshet, 1992; Liepert, 2002; Romanou et al., 2007]; this phenomenon showed a great variability from region to region and was mainly attributed to the increase in anthropogenic aerosols especially in the developing and the densely populated countries. On the other hand, other studies [Wild et al., 2005; Romanou et al., 2007; Zerefos et al., 2009] have shown that the negative tendencies in the solar radiation over Europe and North America have reversed sign in the last decade, a phenomenon that was attributed to the control of emissions and the improvement of air quality. [4] Despite the numerous studies analyzing the solar radiation trends over the globe, very few have been carried out over India. Recently, Padma Kumari et al. [2007] analyzed the variation of the monthly averaged solar radiation values at 12 stations (namely, Trivandrum, Chennai, Goa, Visakhapatnam, Pune, Mumbai, Nagpur, Kolkata, Ahmedabad, Varanasi, Jodhpur and New Delhi) distributed throughout the Indian subcontinent for the period They reported an average reduction of 0.86 W m 2 yr 1, while during winter, premonsoon and monsoon seasons the above reduction was observed to be 0.94 W m 2, 1.04 W m 2 and 0.74 W m 2, respectively; the decrease was greater during 1990s than the 1980s. The significant reduction in ground reaching solar radiation can directly be correlated with an increased presence of aerosol particles due to steep industrialization, vehicular pollution, biomass burning and dust storms over the region [e.g., Padma Kumari et al., 2007; Porch et al., 2007; Badarinath et al., 2007a, 2007b]. [5] Atmospheric aerosols and clouds are the two main factors responsible for modulating the ground reaching solar irradiance [Wild, 2009]. Atmospheric aerosols impact the Earth atmosphere energy budget through several processes; directly by scattering and/or absorbing solar irradiance and indirectly by modifying cloud condensation nuclei and altering cloud optical properties and lifetime [Kazadzis et al., 2009]. Numerous studies [e.g., Wild et al., 2005; Streets et al., 2006; Ruckstuhletal., 2008; Ruckstuhl and Norris, 2009] have highlighted the significant contribution of atmospheric aerosols to solar dimming/brightening phenomena. Stanhill and Cohen [2001] compared latitude estimates of solar radiation changes with latitudinal fossil fuel emissions during the period and found a decrease in the ground reaching solar radiation associated with concurrent increase in fossil fuel emissions. Earlier studies by Menon et al. [2002] and Ramanathan et al. [2005] suggested a significant reduction in the surface warming over the Indian region due to the increase in greenhouse gases. Furthermore, Auffhammer et al. [2006] suggested a 20 25% reduction in rice harvesting over India due to the combined effects of atmospheric brown clouds and greenhouse gases. Apart from atmospheric aerosols, clouds also present high temporal and spatial variability and can cause larger variability in solar irradiance [Matthijsen et al., 2000]. Palle and Butler [2001] analyzed sunshine duration in the period over four stations distributed throughout Ireland and estimated a reduction of 20% in the sunshine duration mainly attributed to a 15% increase in cloud cover. They suggested that increased evaporation rates due to increased sea surface temperature (SST) over the Atlantic Ocean could also be a possible reason for the sunshine decline over the region. Rajeevan et al. [2000] analyzed cloudiness and SST patterns for the period over the Indian Ocean and suggested an increase of 0.15 C per decade in SST and a significant increase of 2.5% per decade in low cloud cover over the equatorial Indian Ocean, while Norris and Wild [2009] reported that the solar dimming/brightening phenomenon over China and Japan was better associated with changes in cloud cover and cloud optical properties. [6] In the present study we have analyzed the trends in net downward shortwave radiation (NDSWR) for the period over the urban region of Hyderabad, India, using the new long term Modern Era Retrospective Analysis for Research and Applications (MERRA 2D) data sets. This is the very first study over the region using long term solar radiation and cloudiness data sets from MERRA and one of the first studies over the globe. Aerosol optical depth (AOD) measurements from a ground based Microtops II Sun photometer and from the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) spaceborne sensor have also been taken into account aid the aerosol trend analysis over Hyderabad in the recent years. 2. Study Area [7] The study area of Hyderabad is located between and N latitude and and E longitude [Sharma et al., 2009]. Hyderabad is the fifth largest city in India; its population is 5,751,780 inhabitants according to the census of 2001, a purely urbanized area. The climate of the region is semiarid with a total rainfall amount of 700 mm occurring mostly during the monsoon season in the period June October. The climatology of the area experiences four dominant seasons, winter (December February), premonsoon (March May), monsoon (June September), and postmonsoon (October November), which strongly affect the type, the load, and the optical properties of the aerosols [Kaskaoutis et al., 2009]. In the winter season the aerosol load is rather moderate and the aerosols are mainly from anthropogenic sources. The premonsoon season is characterized by frequent dust storms and dry spells, while the associated air masses carry dry dust particles from the western Thar Desert to the region. Moreover, the dry weather enforces forest fire outbreaks in the surrounding landscapes [e.g., Badarinath et al., 2004]. Monsoon is the rainy season; despite this the AOD can be large on certain 2of18

3 days, with dominating coarse mode aerosols [Kaskaoutis et al., 2009]. Postmonsoon is characterized by more transparent atmospheric conditions and lower aerosol load. The mean annual temperature exhibits its larger values in April May and its lowest in December January, while the relative humidity is very large (above 70%) in the monsoon season, decreasing to 35 50% in premonsoon. 3. Data Sets and Methodology 3.1. Solar Radiation Measurements [8] For the purpose of monitoring solar radiation worldwide, several radiation monitoring networks have been established throughout the world, for example, the Global Energy Balance Archive (GEBA) [Gilgen et al., 1998], the World Radiation Data Center (WRDC) [Wild, 2009], the Baseline Surface Radiation Network (BSRN) [Ohmura et al., 1998], the Atmospheric Radiation Measurement (ARM) [Ackerman and Stokes, 2003], the Surface Radiation Network (SURFAD) [Augustine et al., 2000], the NOAA Earth System Research Laboratory (ESRL) network [Dutton et al., 2006], the Australian Network and the Alpine Surface Radiation Budget (ASRB) network [Philipona et al., 2004]. Despite the large number of studies, the literature does not cover the entire terrestrial and marine surfaces. In the recent decades, satellite derived estimates of solar radiation can provide a far better spatial coverage than the surface networks and are able to cover the lack of data over oceanic areas. However, satellites can only measure the fraction of solar radiation that is reflected back to space and, therefore, cannot determine the surface solar radiation directly. To this respect, various methods have been developed to derive surface solar radiation from satellites [e.g., Pinker et al., 1995; Rossow and Duenas, 2004; Hatzianastassiou et al., 2004; Arola et al., 2009]. However, useful satellite information has become only available since the early 1980s; several studies have since been performed using such data [Pinker et al., 2005; Hatzianastassiou et al., 2004; Hinkelman et al., 2009]. The MERRA 2D data sets used in the present work are based on satellite observations and reanalyses allowing for a climatological assessment (above 25 years) of solar radiation throughout the world. [9] MERRA is a reprocessing of atmospheric observations [Bosilovich et al., 2008] using a new version of the Goddard Earth Observing System Data Assimilation System (GEOS DAS) [Rienecker et al., 2007]. The data sets used in the present study include the net downward shortwave radiation (NDSWR) in Wm 2 at the surface (incident minus reflected) and the optical depths of low level (<700 hpa), midlevel (700 < h < 400 hpa) and high level (>400 hpa) clouds and have been downloaded from the MERRA 2D achieves ( over Hyderabad with a spatial resolution of 2/3 1/2 degrees. Furthermore, in order to investigate separately the role of aerosols and clouds in NDSWR, we obtained the NDSWR for clean atmosphere (without aerosols) and clear sky (without clouds). The term clean atmosphere refers to fluxes computed with the aerosol loading set to zero, while the latter assuming no clouds. The MERRA data set covers the modern era of remotely sensed data from 1979 to the present and focuses on improved estimates of solar radiation, hydrological cycle and other land, oceanic and atmospheric variables on a broad range of weather and climate time scales. MERRA products are generated as a long term synthesis that places the NASA EOS suite of observations in a climate context. The MERRA analysis is performed at a horizontal resolution of 2/3 1/2 degrees at 72 levels extended from the surface to the stratosphere. The MERRA system employs an incremental analysis update (IAU) procedure [Bloom et al., 1996] that eliminates the shocks that are associated with the insertion of the observations. The reliability of the satellite derived surface solar radiation trends depends, apart from the accuracy of the physical or empirical radiative transfer algorithms, on the accuracy of the observational input data used in the computations, such as cloud and aerosol observations. The uncertainties in representing aerosol effects in satellite derived surface solar radiation may further contribute to some of the disagreement between surface and satellite derived trends [Wild, 2009] Terra MODIS AOD 550 [10] The MODIS sensor is onboard the polar orbiting NASA EOS Terra and Aqua spacecrafts with equator crossing times of 1030 and 1330 Local Solar Time (LST), respectively [Levy et al., 2007]. MODIS has been acquiring daily global data in 36 spectral bands from visible to thermal infrared (29 spectral bands with 1 km, 5 spectral bands with 500 m, and 2 spectral bands with 250 m, nadir pixel dimensions). The aerosol properties are derived by the inversion of the MODIS observed reflectance using precomputed radiative transfer lookup tables based on aerosol models [Levy et al., 2007]. The data used in this study includes the Terra MODIS Level 3 collection 5 (C005) AOD at 550 nm (AOD 550 ) over the urban area of Hyderabad in the period October 2002 to May 2008 to be concurrent with the respective Microtops II AOD measurements Microtops II Sun Photometer AOD [11] The Solar Light microprocessor controlled total ozone portable spectrometer (Microtops II) was used to measure the AOD at the wavelengths of 380, 440, 500, 675, 870 and 1020 nm [Morys et al., 2001]. The instrument measures the irradiance signals at the above wavelengths in mv, from which the absolute irradiance in W m 2 is obtained by multiplying the signal with the calibration factor (W m 2 mv 1 ). The calibration relies on a high performance voltage reference with a temperature coefficient of 0.001% per degree Celsius and a long term stability of 0.005% per year. The long term stability of the UV filter is better than 0.1 nm yr 1, while the accuracy of the Sun targeting angle is better than 0.1. The Full Width at Half Maximum (FWHM) bandwidth for the 380 nm channel is 2.4 ± 0.4 nm and 10 ± 1.5 nm for the other channels. In order to avoid any errors in the Sun targeting angle, the Microtops II was mounted on a tripod stand throughout the experimental period. The Sun photometer works on the principle of measuring the solar radiation intensity at the specified wavelengths and converts it to optical depth by knowing the corresponding intensities at the top of the atmosphere (TOA), using its internal calibration. The TOA irradiances at each wavelength are calculated via the well known Langley method, while Kasten and Young s [1989] expression for the 3of18

4 Figure 1. Mean annual values of the net downward shortwave radiation (NDSWR) over the urban region of Hyderabad during : (a) all conditions, (b) clean atmosphere (without aerosols), and (c) clear sky (without clouds). The whole period is divided into two subperiods ( and ). The regression equations for each period are also given. air mass is used in those calculations. The ground based AOD measurements over Hyderabad cover the period October 2002 May Results 4.1. Trend in NDSWR Over Hyderabad [12] Figures 1a 1c show the time series of MERRAderived NDSWR over Hyderabad for all sky (Figure 1a), clean atmosphere (no aerosols, Figure 1b), and clear sky (no clouds) conditions (Figure 1c) during the period The annual mean NDSWR values used in the analysis were computed from the monthly mean values; the time series have been subjected to linear regression analysis. Owing to the remarkable differences in the NDSWR trends observed in Figure 1, the 27 year period has been divided in two subperiods ( and ), which are analyzed separately in the following. Regarding Figure 1a, the linear trend exhibits strong decline (statistically significant at the 95% significance level) during the period ; however, in the period it is almost zero. The average solar dimming observed over Hyderabad is W m 2 yr 1 or W m 2 in the period However, during the period a strong decline of 1.55 W m 2 yr 1 or a total of W m 2 is observed. The average reduction in solar radiation over Hyderabad was found to be W m 2 for the period The above values are translated to a decrease of 9.7%, 0.49% and 22.4% in the NDSWR for the periods , and , respectively. The decrease in the second subperiod is very large compared with other studies in the literature (see review by Wild [2009]) and much effort has been made in order to examine the role of aerosols and clouds in such a large solar radiation decline. Figure 1b shows the variation in NDSWR assuming cleanatmosphere conditions, thus highlighting the influence of clouds. The diachronic patterns as well as the regression equations are similar to the previous case, exhibiting little greater slope values. Regarding the % reduction in NDSWR the values are similar to the previous case ( 9.74%, 0.49% and 23.1%) for the periods , and , respectively. However, at this urban location the aerosol loading is large [Kaskaoutis et al., 2009] and, therefore, it strongly affects the solar radiation amounts reaching the ground [Badarinath et al., 2007a, 2007b]. The influence of aerosols in the NDSWR values can be revealed from Figure 1c. Although the yearly NDSWR values do not differ significantly, ranging from to W m 2, a negative 4of18

5 Figure 2. (a d) Seasonal trends in NDSWR for all sky conditions over Hyderabad in the period and the two subperiods ( and ). The linear regressions are also given. trend in NDSWR is revealed, with greater inclination in the period This introduces a decrease of 1.08 W m 2 or 0.95% in the NDSWR due to aerosols during the period , which is much smaller than the decrease observed for all sky conditions (Figure 1a). However, this decrease is 1 order of magnitude larger than that observed in the first period ( W m 2 yr 1 ) and, therefore, the role of aerosols in controlling solar radiation over this urban area cannot be ignored. These trends are indicative of an increase in aerosols in the second half of the studied period. On the other hand, it is found that the decline in NDSWR attributed to aerosols can explain only about 5.3% of the NDSWR (all skies) during the period , while the respective fraction in the first period ( ) is 22%. This indicates that the cloud variability is the most important factor controlling solar radiation over Hyderabad. [13] Although Hyderabad is a tropical location, the amount of solar radiation exhibits large differences within the year mainly driven by the local monsoons. To this respect, the diachronic variation of the NDSWR is further analyzed for each season in Figures 2a 2d. Large differences in the NDSWR trends appear among the four dominant seasons, as well as in the whole study period and in the two subperiods. The large peaks and gaps in NDSWR, even in the same month, are attributed to the intense or weak monsoon years, which also affect the aerosol load over Southeast Asia [Zhang et al., 2010]. In winter (Figure 2a), the trend in NDSWR is very small (statistically insignificant, 1.19%), and a slight increase is observed in the period ; however, this positive trend changes to negative in the second period ( ). The decreasing trend is more pronounced in premonsoon (Figure 2b) and especially in the second subperiod ( 8.49%), while during the monsoon period (Figure 2c) the variation in NDSWR exhibits large differences between the two subperiods; a rather neutral pattern ( 1.09%) in the first half ( ) and a 5of18

6 Figure 3. (a) Monthly variation of the NDSWR over Hyderabad for all sky, clear sky (without clouds), and clean atmosphere (without aerosols) conditions for the period The vertical bars express one standard deviation from the mean. (b) Mean monthly variability of the level 3 Terra MODIS AOD 550 for the period and mean monthly variability of the ARF derived from MERRA reanalysis for the period over Hyderabad. large decrease ( 22.4%) afterward. The strong influence of clouds in the monsoon season over the Indian subcontinent as well as their variability from year to year [Bhawar and Devara, 2009] seems to control the amount of NDSWR and its diachronic trend over Hyderabad. In postmonsoon (Figure 2d) the trends lie between the above cases. From the above analysis a strong decrease in NDSWR is revealed over Hyderabad during the period (Figure 1a) that mainly occurs in the monsoon and premonsoon seasons. The reasons for this decrease and the combined role of aerosols and clouds are investigated in section 4.2. In contrast, regarding the first period ( ) the solar radiation presents negligible variations in monsoon and postmonsoon, a slight increase in winter (3.58%) and a decline in premonsoon ( 5.19%). However, in all seasons these variations are much smaller than those observed in the second half of the studied period Factors Controlling the Variations in Solar Radiation [14] Changes in the surface solar radiation can either be caused by changes in the extraterrestrial solar radiation at TOA, or by internal changes in the transparency of the atmosphere [e.g., Ohvril et al., 2009]. Changes in the extraterrestrial solar radiation are due to the Earth s orbital parameters and the 11 year solar cycle. Variations in the atmospheric transparency can be attributed to changes in the cloud optical properties, in the radiative active gases (particularly the water vapor), and in the concentration, the optical and physicochemical properties of the aerosols. Various satellite sensors have directly measured variations in the extraterrestrial radiation since the early 1980s and they have shown a periodic signal related to the 11 year solar cycle with an amplitude of about 1 W m 2. However, these variations are at least an order of magnitude smaller than the changes detected from surface observations of solar radiation [e.g., Frohlich and Lean, 1998; Wilson and Mordinov, 2003]. Therefore, extraterrestrial influences can be neglected in the interpretation of the observed variations in solar radiation reaching the surface. Even though the water vapor has the largest potential to modify solar radiation, sensitivity studies using radiative transfer models indicated that considerable changes in water vapor would be necessary to explain the observed surface solar radiation trends. Thus, a 10% increase in the atmospheric water vapor content would decrease solar radiation at the surface by less than 0.5%, corresponding to less than 1 W m 2 on a global mean basis [Wild, 1997]. Similarly, the changes in atmospheric trace gas concentrations from the preindustrial era to the present have had a minor effect on changes in the surface solar radiation. All the above suggest that aerosols and clouds are the most likely candidates for the explanation of the global dimming and brightening phenomenon. These two parameters are not completely independent, as aerosols and clouds can interact in various ways affecting the hydrological cycle [e.g., Ramanathan et al., 2001]. [15] Figure 3a shows the monthly mean values of the NDSWR under all skies, without clouds and without aerosols. The monthly variation of the NDSWR for all sky conditions and assuming no aerosols exhibit the same pattern with larger values in premonsoon and lower in monsoon and winter. These curves are, more or less, in close agreement with the annual variability of solar radiation over 12 Indian cities presented by Padma Kumari et al. [2007], while taking into account the standard deviations, the present curves are well within those measured at the 12 Indian cities. More specifically, the monthly variability over Hyderabad obtained from MERRA 2D reanalysis is much closer to that for Visakhapatnam, which is the closest station to Hyderabad. The lower monthly mean values obtained from MERRA reanalysis ( W m 2 on monthly basis) compared to those presented by Padma Kumari et al. [2007] are probably attributed to the reflected radiation. The general agreement both in the values and in the variation of MERRA 2D NDSWR over Hyderabad with the solar radiations measured over 12 Indian cities gives credit to the MERRA 6of18

7 Figure 4. Monthly variation of (a) COD over Hyderabad for all clouds, low level clouds, midlevel clouds, and high level clouds and (b) fraction of the low level, midlevel, and high level clouds COD to total (all clouds) COD. retrievals and renders them appropriate for such studies. However, a degree of uncertainty, which is difficult to be defined owing to lack of long term solar radiation measurements over Hyderabad, still exists, but the present work aims to investigate the trends in NDSWR qualitatively, rather than to provide a quantitative assessment of the NDSWR values. Furthermore, since MERRA reanalysis is a newly released data set, there is lack of comparative studies with observations. The difference between the two curves ( 11.3 W m 2 on average) is attributed to aerosol attenuation (aerosol radiative forcing, ARF), which is larger in premonsoon ( 15.5 W m 2 ) because of the enhanced aerosol loading in this period over Hyderabad [Kaskaoutis et al., 2009]. The clean sky NDSWR from MERRA reanalysis assumes zero AOD; thus, it is necessary to know how well MERRA represents the aerosol effects, since reanalysis often does not capture the full extend of ARF. The quantification of this effect is rather difficult to be defined since aerosol load and properties are very uncertain over the globe and above urban sites like Hyderabad, also changing significantly depending on season [Kaskaoutis et al., 2009]. In order to have a first check on it we compared the monthly mean ARF values obtained from MERRA (NDSWR no aerosols NSDWR all skies ) with the Terra MODIS AOD 550 in the period (Figure 3b). The ARF presents larger negative values in April June, while the AOD 550 is higher in summer monsoon (June July), presenting large values in premonsoon. In general, both the values and the monthly variation of the Terra MODIS AOD 550 over Hyderabad agree with those observed over Chennai [Ramachandran, 2007]. The ARF values presented in Figure 3b are reliable assuming monthly mean radiation values and are similar in magnitude, or even lower, to those observed over the Bay of Bengal [e.g., Vinoj et al., 2004; Moorthy et al., 2009]. On the other hand, Figure 3b shows an inconsistency between the monthly variation of the ARF and AOD 550 values exhibiting a steady ARF ( 10 W m 2 ) from June to December, although the AOD 550 is reducing. However, except from the AOD 550, the single scattering albedo plays a major role in the ARF. A recent study over Hyderabad [Kaskaoutis et al., 2009] showed that the aerosols in monsoon consisted mainly of coarse particles of marine origin mixed with anthropogenic ones, while in premonsoon there is stronger possibility for fine mode aerosols (urban and biomass burning). It is therefore believed that the premonsoon aerosols over Hyderabad would be more absorbing, and thus, can cause larger attenuation to solar radiation reaching the ground for a given AOD. This fact can, therefore, conclude to a higher ARF in premonsoon months (April May). However, this must be checked in more detail; the present analysis provides only an evidence of the MERRA retrievals. Since the uncertainties in these retrievals are very difficult to be quantified, further analysis of the NDSWR under clean skies (no aerosols) is avoided. Although the differences between the three curves are generally small in winter and premonsoon, they are very large in monsoon, reaching 95 W m 2 in August between cloudy and cloudless conditions. This highlights the important role of clouds in the attenuation of solar radiation in the monsoon season as well as the significant role of the year to year cloud variability in the NDSWR values. [16] However, all clouds do not attenuate solar radiation in the same way. The cloud thickness, the cloud albedo, the cloud base are the main factors controlling the attenuation of solar radiation. For this reason the cloud optical depth (COD) is taken into account. In Figure 4a the mean monthly variation of COD is shown for all, low level (<700 hpa), midlevel (700 < h < 400 hpa) and high level (>400 hpa) clouds. The COD of the midlevel clouds takes larger values compared to the other two categories and seems to control the annual variability of COD of all clouds. Independent from the cloud base height, COD is generally low in winter, decreasing further in premonsoon to values lower than 10. However, after May COD is increasing dramatically to values above 45 (COD all clouds) in August. The large COD value for all cloud levels is a general characteristic in the monsoon season. In postmonsoon, COD decreases, but its values become much larger than those in winter and premonsoon. Figure 4b shows the monthly mean contribution of each cloud level to the COD for all clouds. It is quite characteristic to observe that in winter, the low level clouds control total COD mainly, with their contribution being very low in premonsoon. The contribution of the midlevel clouds is high in all seasons except winter, reaching in monsoon 7of18

8 Figure 5. Mean annual COD values over Hyderabad for (a) all clouds, (b) low level clouds, (c) midlevel clouds, and (d) high level clouds during the period The whole period is divided into two subperiods ( and ). The regression equations for each period are also shown. and 0.7 in premonsoon. Although the total COD is very low in premonsoon, the high level clouds possess a similar fraction with the low level ones in this season. Furthermore, the high clouds contribute 11% to the total COD in monsoon. Owing to the large differences in the COD values, the great seasonality as well as the different contributions to the total COD, the diachronic fluctuation of COD and its correlation with the NDSWR are analyzed separately for the three cloud levels Influence of Clouds [17] Clouds constitute the strongest modifiers to the surface solar radiation; the variation in cloudiness plays a key role in understanding climate change [Liepert, 2002]. Figures 5a 5d show the year to year variation of the MERRAderived COD over Hyderabad during for all clouds (Figure 5a), low level clouds (Figure 5b), midlevel clouds (Figure 5c), and high level clouds (Figure 5d). Similar to the above, the whole period has been divided into two subperiods. The trend lines superimposed on each plot are determined by linear regressions. The midlevel clouds govern the temporal variation of the COD of all clouds mainly because of their larger thickness. The correlation coefficient between the optical depth for midlevel and all clouds is 0.92, while that for the low level and all clouds is Large year to year variability in the optical depth of all clouds, ranging from 21.5 to 31.5, is observed over the study region; similarly, a large variability is revealed for the low level and midlevel clouds, while the high level ones show a continuous increasing trend in optical depth after 1993 with rather insignificant peaks and gaps. [18] In general, an increasing trend in COD for all (6.85%), midlevel (7.38%) and high level clouds (70.86%) is observed, while the low level ones suggest a decrease of 11.89% over Hyderabad, during the period All and midlevel clouds exhibit different trends in the two subperiods. A common characteristic is the increasing trend in COD in the period and a decrease afterward, while the low level clouds present negative COD trends in both periods. These decreases are rather significant since COD decreases at 3.41, 2.89 and 1.25 for all, lowlevel and midlevel clouds, respectively, corresponding to decreases of 12.9%, 30.3% and 9% from 1993 to These decreases are inconsistent with the observed strong decline of NDSWR in the same period ( W m 2 ). In further contrast, the COD of the high level clouds presents a pronounced increase of 2.21 or 66.2% in the 13 year period ( ). This increase can partly explain the large 8of18

9 Figure 6. (a d) Mean seasonal values of COD (all clouds) over Hyderabad during the period The linear regression for the whole period is shown, where a indicates the slope of the fitted lines. decline in NDSWR since high clouds can strongly reflect solar radiation back to space. [19] The local monsoons present significant variations in the intensity and amount of rainfall from year to year, as well as in cloud cover and optical thickness [Bhawar and Devara, 2009]. The sea surface temperature (SST), the ocean atmosphere dynamics and the position and the movement of the Inter Tropical Convergence Zone (ITCZ) play a significant role in the arrival, intensity and amount of rainfalls of the Indian monsoons [Krishnamurti et al., 1998]. On the other hand, the aerosols, both natural and anthropogenic, play an important role in the local monsoon system (onset of the monsoon, intensity, rainfall amounts and distribution), as numerous studies over South Asia have shown [e.g., Menon et al., 2002;Lau et al., 2006; Gautam et al., 2009; Prasad et al., 2009; Zhang et al., 2010]. Furthermore, the presence of intense tropical cyclones over the Bay of Bengal [Badarinath et al., 2008, 2009a] affects strongly the cloud cover over the region as well as the SST [Badarinath et al., 2009b]. In late 1980s, the COD over Hyderabad is low, while in the beginning and the middle of 1990s it was high. Actually, 1987 and 1988 were two dry years for Hyderabad with limited monsoon period, cloud cover and precipitation. The clouds in the monsoon season are mainly detected at the lower and middle troposphere (Figures 4a and 4b), with cumulus and nimbocumulus as dominating cloud types. Aerosols have a rather small, or even negligible, role in COD and cloud properties in this season, but they may play a significant role in the other seasons (e.g., premonsoon) altering the cloud lifetime and properties, the precipitation amount and the hydrological cycle [e.g., Ramanathan et al., 2001, 2005; Gautam et al., 2009]. The large observed increase in the optical depth of the high level clouds from the middle 1990s to 2005 may be associated with increased aerosol emissions over the region. [20] Owing to the great seasonality in COD (Figures 4a and 4b), its temporal variation in each season is shown in Figures 6 9 for all, low level, midlevel, and high level clouds, respectively. In Figures 6 9 the trend for the whole period ( ) is only plotted, while the slope (a) values for each period are given. Regarding all clouds (Figure 6) an increase in COD is observed only in the premonsoon and postmonsoon seasons; in contrast, in winter and monsoon a rather neutral pattern occurs. Viewing the slope values, it seems that COD decreases in the monsoon and postmonsoon seasons in the second half ( ) of the study period. For this period an increase, although limited owing to smaller COD values, is observed in premonsoon. It is worth to be noted that winter shows negative trends in COD (all, mid and low) and a neutral trend in high clouds during the period This would be the reason for the increase in NDSWR (Figure 2a), which is the only increase observed over Hyderabad. Regarding the low clouds (Figure 7), the winter and mon- 9of18

10 Figure 7. (a d) Same as Figure 6 but for the low level clouds. soon seasons present negative trends for both subperiods, dominating the mean annual trend observed in Figure 5b. In postmonsoon, an even stronger decrease in COD values is found from 1993 to The slight increase observed in premonsoon cannot balance the aforementioned decreases regarding the low level COD over Hyderabad. The midlevel clouds (Figure 8) have the largest COD values and, therefore, cause the larger reduction in NDSWR. However, aiming at explaining the large decline in NDSWR in the period based on changes (increase) in COD for midlevel clouds is not feasible since COD in this period shows either slight positive or even negative (monsoon, postmonsoon) trend. In contrast to all the above, Figure 5d showed a significant increase in COD for the high level clouds in the second half of the studied period. This increase is attributed mainly to the monsoon and premonsoon seasons (Figure 9), while in winter and postmonsoon this is more limited. In contrast, in the period all seasons show a negligible trend in COD (high clouds) consisted with Figure 5d. [21] Figures 10a 10d show a scatterplot of the mean monthly NDSWR values versus COD for all clouds (Figure 10a), low level clouds (Figure 10b), midlevel clouds (Figure 10c), and high level clouds (Figure 10d) over Hyderabad covering the period The four seasons together with linear trends are plotted in different colors, while the all cloud linear trend is plotted in black. The statistical parameters, slope (a), intercept (b), and coefficient of determination (R 2 ) of the linear regressions for the four seasons are given in Table 1. [22] Independent of the cloud level, a significant negative trend between NDSWR and COD appears. Regarding COD for all clouds (Figure 10a), the analysis shows a good correlation (R 2 = 0.48), although a large scatter is observed. The main reasons for this large scatter are the various solar elevation angles and sunshine durations causing remarkable changes in NDWSR in all seasons. The largest NDSWR values were observed in premonsoon associated with low COD values. The correlation between NDSWR and COD in premonsoon is strongly negative (r = 0.75), a characteristic that is independent from the cloud level (see a and R 2 values in Table 1). The respective correlations in winter and postmonsoon are rather low with a large scatter, while the NDSWR values range significantly (between 150 and 230 W m 2 in winter and between 140 and 200 W m 2 in postmonsoon). The monsoon season shows no correlation between NDSWR and COD for the all clouds case, with the NDSWR and COD values varying in a wide range. Nevertheless, the analysis shows that NDSWR decreases at 1.25 ± 0.07 W m 2 for a unit increase in COD in the all clouds case. [23] A strong negative trend in NDSWR for increasing COD of low clouds is also observed (Figure 10b), although this COD shows a decreasing trend (Figure 5b) over the study region. The negative trend, associated with 53% of the 10 of 18

11 Figure 8. (a d) Same as Figure 6 but for the midlevel clouds. variance, is mainly driven by large NDSWR values (above 220 W m 2 in the vast majority of the cases) for COD values below 5 (premonsoon). On the other hand, for larger COD values (>10) the scatter is very large without any evident trend for winter, monsoon and postmonsoon. Furthermore, a positive (although negligible) trend between NDSWR and COD for low clouds in monsoon is observed, which is rather strange. The scatterplot between midlevel COD and NDSWR (Figure 10c) exhibits large similarities to that regarding COD of all clouds, r = The trend line reveals a remarkable decrease in NDSWR of 1.84 ± 0.14 W m 2 for a unit increase in the midlevel COD. The large scatter in the correlations between NDSWR and COD (all, mid and low clouds) in winter and monsoon is attributed to the inconsistency in the temporal variation between them. Note that NDSWR decreases in winter and especially in monsoon over Hyderabad (Figures 2a and 2c), while COD (all, mid, low clouds) presents a zero or even a negative trend (Figures 6 8). This fact causes the large scatter in the correlations observed, while in monsoon the slight positive correlation (Figure 10b) can be explained by the concurrent decreases in NDSWR (Figure 2c) and COD (low clouds) (Figure 7), especially for the period Furthermore, in postmonsoon the negative trend in NDSWR (Figure 2d) is associated with a general positive (despite the negatives in ) trend in COD for all, low level and midlevel clouds (Figures 6 8); thus the R 2 values are higher ( ). [24] In contrast, a difference from the previous cases appears as regards the variation of NDSWR with the highlevel COD (Figure 10d). The influence of the season in NDSWR versus COD is pronounced, since some groups of data points are formed. Thus, for COD values below 2, the NDSWR can be high (>230 W m 2 ) in premonsoon or low (<200 W m 2 ) in winter and postmonsoon, while the vast majority of the cases (COD > 3) belong to monsoon. In monsoon, NDSWR exhibits a remarkable decrease without large scatter (R 2 = 0.83), which is opposite to that observed in the previous cases. It is quite characteristic that for COD > 4 the NDSWR is always below 180 W m 2. Furthermore, the correlation in premonsoon is stronger than that for the other cloud levels (Table 1). This fact highlights the strong influence of the high level clouds on the amount of solar radiation at the Earth s surface. [25] The main finding from the above analysis is that the large decline in NDSWR over Hyderabad from 1993 to 2005 is mainly attributed to the increase in the high level COD in the premonsoon and monsoon seasons (Figure 9) if the role of aerosols is excluded. This is also verified in Figures 2b and 2c, which show a significant decrease in NDSWR for the premonsoon and monsoon seasons in the second half ( ). The strong negative correlation between COD (high clouds) and NDSWR (Figure 10d) 11 of 18

12 Figure 9. (a d) Same as Figure 6 but for the high level clouds. mainly in monsoon and second in premonsoon gives also credit to the above statement. Therefore, it is established that the significant increase of the high level CODs in the last years may play the most important role in the solar dimming over Hyderabad. However, in the observed changes in COD, nobody can ignore the role of aerosols, which, through their indirect and semidirect effects, modulate the cloud optical properties and especially those of the high clouds. [26] Other studies over the globe have reached similar results with the present analysis. Cess et al. [1995] analyzed collocated satellite and surface measurements of solar radiation and suggested a 25 W m 2 reduction in global solar radiation due to clouds. Similarly, Liepert [2002] and Palle and Butler [2001] also observed a strong decline in downward solar radiation associating it with increased cloud cover. A recent study over China and Japan [Norris and Wild, 2009] has proved that the variations in cloudiness and cloud properties (optical depth, albedo) are most responsible for the changes in solar radiation reaching the ground. However, large decadal changes in solar radiation are not found only for all sky conditions (including clouds), but also for cloudless atmospheres [e.g., Ohvril et al., 2009; Zerefos et al., 2009] indicative of anthropogenic contributions through changes in aerosol emissions governed by economic development Influence of Aerosols [27] Despite the aforementioned results, some regions do not exhibit trends in cloud cover that have opposite sign from trends in solar radiation [e.g., Stanhill and Moreshet, 1994; Norris and Wild, 2007], indicating that changes in cloud cover can partly explain the changes in surface solar radiation. The AOD values obtained from ground based and remote sensing instruments have been used for finding the aerosol trends over Hyderabad during the period Figure 11a shows the temporal variation of the daily Terra MODIS AOD 550 and Microtops II AOD 550 values obtained at 1030 LST (within a time interval of ±30 min). The initial AOD 500 values from Microtops II were calculated at 550 nm using the Ångström exponent value in the interval nm. The monsoon period (June September) has been omitted from the analysis since the permanent cloud cover obscures Sun photometric and satellite measurements. [28] The results reveal a rather significant day to day variability in all AOD 550 values, underlying the influence of varying aerosol load. The linear regressions of the AOD 550 values are shown in Figure 11a, as well as the DAOD 550, which expresses the percent difference of AOD 550 between the start and the end of the study period. A pronounced increasing trend in the Microtops II AOD 550 (34.3%) and in MODIS AOD 550 (20.2%) is observed during the last 6 years over Hyderabad, which is interpreted as and increase in absolute AOD 550 values, respectively. Both 12 of 18

13 Figure 10. Correlation of the mean monthly NDSWR values with COD for (a) all clouds, (b) low level clouds, (c) midlevel clouds, and (d) high level clouds over Hyderabad during the period The seasons are given in different colors. The linear regression equation for the whole period is given in the graphs. trends are found to be statistically significant at the 95% confidence level, underlying a significant increase in aerosol load over Hyderabad in the last 6 years, mainly attributed to the economic growth and industrialization, population growth, increased vehicular emissions and expansion of the city. This increasing trend is opposite to that observed in the AOD over Mediterranean [Papadimas et al., 2008] and Thessaloniki, Greece (decreasing rate of 4.5% yr 1 ), but it is in agreement with the increasing AOD trend (1.0% yr 1 ) over Beijing between 2000 and 2008 [Zerefos et al., 2009]. Such a large increase in AOD over an urban area with surrounding industrial activities may be a significant reason for the solar dimming over Hyderabad observed in the recent years, even under clear (without clouds) atmospheres (Figure 1c). [29] The AOD 550 trends were further analyzed focusing on each season. In winter (Figure 11b), the increasing trends are significant, especially for the Microtops II AOD 550, corresponding to a 53% and 28.4% increase in AOD 550 values from Microtops II and MODIS, respectively. It may be noted that after 2005, the winter AOD 550 exhibits more peaks (>0.8) characteristic of a pollution smog environment. In winter, the aerosols over Hyderabad are mainly concentrated within the boundary layer, composed of a large Table 1. Statistical Parameters From the Linear Regression Between NDSWR and COD for Each Season a a b R 2 NDSW Radiation Versus COD (All Clouds) Winter Premonsoon Monsoon Postmonsoon NDSW Radiation Versus COD (Low Level Clouds) Winter Premonsoon Monsoon Postmonsoon NDSW Radiation Versus COD (Midlevel Clouds) Winter Premonsoon Monsoon Postmonsoon NDSW Radiation Versus COD (High Level Clouds) Winter Premonsoon Monsoon Postmonsoon a The a is slope, b is intercept, and R 2 is coefficient of determination. 13 of 18