Trends in solar radiation due to clouds and aerosols, southern India, 1952–1997

Abstract

Abstract:Decadal trends in cloudiness are shown to affect incoming solar radiation (SW SFC ) in the Krishna River basin (13-20°N, 72-82°E), southern India, from 1952 to 1997. Annual average cloudiness at 14 meteorological stations across the basin decreased by 0.09% of the sky per year over 1952-1997. The decreased cloudiness partly balanced the effects of aerosols on incoming solar radiation (SW SFC ), resulting in a small net increase in SW SFC in monsoon months (0.1-2.9 W m −2 per decade). During the non-monsoon, aerosol forcing dominated over trends in cloud forcing, resulting in a net decrease in SW SFC (−2.8 to −5.5 W m −2 per decade). Monthly satellite measurements from the International Satellite Cloud Climatology Project (ISCCP) covering 1983-1995 were used to screen the visual cloudiness measurements at 26 meteorological stations, which reduced the data set to 14 stations and extended the cloudiness record back to 1952. SW SFC measurements were available at only two stations, so the SW SFC record was extended in time and to the other stations using a combination of the Angstrom and Hargreaves-Supit equations. The Hargreaves-Supit estimates of SW SFC were then corrected for trends in aerosols using the literature values of aerosol forcing over India. Monthly values and trends in satellite measurements of SW SFC from National Aeronautics and Space Administration's (NASA's) surface radiation budget (SRB) matched the aerosol-corrected Hargreaves-Supit estimates over 1984-1994 (RMSE = 11.9 W m −2 , 5.2%). We conclude that meteorological station measurements of cloudiness, quality checked with satellite imagery and calibrated to local measurements of incoming radiation, provide an opportunity to extend radiation measurements in space and time. Reports of decreased cloudiness in other parts of continental Asia suggest that the cloud-aerosol trade-off observed in the Krishna basin may be widespread, particularly during the rainy seasons when changes in clouds have large effects on incoming radiation compared with aerosol forcing.

Climate change scenarios of surface solar radiation in data sparse regions: a case study in Malaprabha River Basin, India

“…The calibrated parameters were comparable to those from earlier studies. In the study by Biggs et al (2007), the para meter values in the entire Krishna basin estimated by using the MH method were A s = 0.045, B s = 0.20, and C s /R exo = 0.15. The para meters in the DB method obtained from SI-PEAA (2004) for Hyderabad were 0.69, 0.0846, 0.029, and 0.008 for τ, b, c 1 , and c 2 , respectively.…”

“…These methods use measured meteorological variables (temperature and CC) and the parameters listed in Section 3.1. The parameter values for these models were taken from other studies (Donatelli & Bellocchi 2000, Biggs et al 2007) covering the MRB. The proposed methodology for Case 3 for estimating SR in the absence of measured data was tested with the satellite measurements provided by Biggs et al (2007) for the region.…”

“…The parameter values for these models were taken from other studies (Donatelli & Bellocchi 2000, Biggs et al 2007) covering the MRB. The proposed methodology for Case 3 for estimating SR in the absence of measured data was tested with the satellite measurements provided by Biggs et al (2007) for the region. Their study obtained SR from NASA's SRB Release 2 at 1° resolution from March 1984 to September 1995.…”

“…The proposed methodology in Case 3 (estimating SR in the absence of measured data) was validated by comparison with the observed data for the region from Biggs et al (2007) (Fig. 5).…”

“…Solar radiation (SR; see Table 1 for further abbreviations) plays an important role in hydrological, ecological, biological, and physical processes (Rivington et al 2005, Biggs et al 2007, Teuling et al 2009, Wild 2009). The lack of observed SR data, especially in developing countries, has hindered various climate change impact studies in agricultural and hydrological sectors , thus indicating a need to estimate future scenarios of SR in situations where observed SR data are sparse.…”