Recent Changes in Downward Longwave Radiation at King Sejong Station, Antarctica

Abstract

Effects of cloud, air temperature, and specific humidity on downward longwave irradiance and their long-term variabilities are examined by analyzing the measurements made at the King Sejong Station in the Antarctic Peninsula during the period of 1996-2006. It has been shown that the downward longwave irradiance (DLR) is significantly correlated with three variables: air temperature, specific humidity, and cloudiness. Based on the relationship of the three variables with DLR, a multiple linear regression model has been developed in order to evaluate the relative contribution of each of the variables to the variation of DLR. The three variables together explained 75% of all the variance in daily mean DLR. The respective contribution from specific humidity and cloudiness to the variation of DLR was 46% and 23%; thus most of the DLR variability can be explained by the variations in the two variables. The annual mean of longwave cloud forcing shows 52 W m Ϫ2 with no remarkable seasonal cycle. It is also noted that the overcast cloud effect gives an increase by 65 W m Ϫ2 with respect to clear-sky flux throughout the year. It is suggested that the multiple regression model can be used to estimate the radiative forcings of variables influencing the DLR variability.A highly significant decrease in DLR with an average of Ϫ1.52 W m Ϫ2 yr Ϫ1 (Ϫ0.54% yr Ϫ1 ) is found in an analysis from the time series of the deseasonalized monthly mean values. Accordingly, the atmospheric flux emissivity, air temperature, and specific humidity have also decreased in their time series, while the cloudiness has increased insignificantly. Consequently, it may be concluded that the recent decrease in DLR is mainly attributed to the net cooling effect due to the decrease in air temperature and specific humidity, which overwhelm the slight warming effect in cloudiness. Analysis of mean monthly trends for individual months shows that, as for the annual data, the variations in DLR are mostly associated with those of air temperature, specific humidity, and cloudiness.

Influence of cloud fraction and snow cover to the variation of surface UV radiation at King Sejong station, Antarctica

“…In terms of the UV radiation related to the stratospheric ozone depletion, this station is located in the range of polar vortex area and thus largely affected by the Antarctic ozone hole during the austral springtime. Compared to other Antarctic inland stations, the King Sejong Station shows relatively warmer and more humid air conditions (Cho et al, 2008), and more frequent cloud condition compared to the Antarctic inland (Choi et al, 2008), thereby revealing the characteristics of an oceanic climate better. The climatology of local meteorological pattern (1988)(1989)(1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006) is as in the following: − 1.6°C for the surface temperature, 8.0 m/s for the surface wind speed, 89% for the relative humidity, and 6.7 oktas for cloud fraction.…”

Trends in surface radiation and cloud radiative effect over Switzerland in the past 15 years

“…The increase in cloud-free DLR was mainly related to the corresponding temperature increase. On the other hand, it has been shown that the all-sky DLR is also strongly related to regional temperature, humidity and cloudiness effects which can even result in a negative trend despite rising greenhouse gas concentrations [7].…”

Spatio-Temporal Characteristics in the Clearness Index Derived from Global Solar Radiation Observations in Korea

“…Thus, the objective of this study is to evaluate spatio-temporal patterns in KT over a recent time period together with long-term trends in KT in Korea. Using a multiple regression model for KT, we also estimate the individual contributions of several atmospheric parameters influencing KT [30].…”

“…We also conducted multiple linear regressions and analyzed partial correlation coefficients in order to estimate the individual contributions of the parameters influencing K T [30]. Table 1.…”

“…This result suggests that all three variables significantly affect K T and that CA is the most influential. Cho et al [30] developed a multiple regression model to evaluate the effects of three variables on downward longwave radiation using daily temperature, specific humidity, and cloud amount at King Sejong Station, Antarctica. Similarly, in this study we have developed multiple regression models to evaluate the individual contributions of clouds, water vapor, and aerosols to K T variations for the 15 years from 2000 to 2014, using the coefficients of partial correlation (r) and the beta coefficients (β) from the statistical equation, R 2 = β 1 r 11`β2 r 12`β3 r 13`β4 r 14 (cf.…”