A Satellite View of the Radiative Impact of Clouds on Surface Downward Fluxes in the Tibetan Plateau

Abstract

Using 13 yr of satellite observations for the Tibetan Plateau, the sensitivities (or partial derivatives) of daytime surface downward shortwave and longwave fluxes with respect to changes in cloud cover and cloud optical thickness are investigated and quantified. Coincident cloud and surface flux retrievals from the NASA Moderate Resolution Imaging Spectroradiometer and the Clouds and the Earth's Radiant Energy System, respectively, as well as ground-based observations at 11 stations across the plateau are used to examine the spatial and seasonal variability of this sensitivity over the entire plateau. The downward shortwave flux is found to be modulated primarily by changes in cloud cover, but changes in optical thickness also have an impact, as revealed by a multiple regression fit. The coefficient of determination of the regression increases by more than 15% when optical thickness is added. There is significant seasonal and regional variability in the cloud radiative impact. On average, at all stations, the sensitivity of surface shortwave flux to changes in cloud cover is about 20.5 6 0.1 W m 22 % 21 in winter according to both ground-based and satellite observations but in summer reaches 21.5 6 0.3 and 21.8 6 0.2 W m 22 % 21 according to ground-based and satellite observations, respectively. Cloud cover itself has little impact on the sensitivity when clouds are optically thin, but above an optical thickness of 12, sensitivities increase with both cloud cover and cloud optical thickness. The daytime longwave flux response to changes in cloud properties is also examined. The radiative impact of a decrease in cloud cover on the surface net flux can be offset or even canceled if cloud opacity increases by 5%-10%.

Climatology of Cloud Phase, Cloud Radiative Effects and Precipitation Properties over the Tibetan Plateau

“…Based on the statistic results of Figure 1, we found that clouds are more distributed during the warm season than the cold season, and this finding may be due to a plentiful supply of moisture during the warm season [101]. In addition, based on long-term cloud records from NASA Clouds and CERES, Naud et al [102] recorded a higher cloud fraction in the northern part of the TP in the winter and a lower cloud fraction in the summer, findings which are consistent with our results. Our results also indicated that cold rain caused by ice clouds dominated precipitation frequency over the TP during the cold season [14] (see Figure 9); Choi et al [19] highlighted that an increase in the dust frequency during the cold season (e.g., winter and spring seasons) over mid-latitude regions of the Northern Hemisphere is derived from a decrease in the supercooled water cloud fraction that effectively glaciates supercooled water clouds by lifted dust aerosols.…”

“…For the TP regions, the super-cooled water cloud is more than the warm water cloud, especially in the southeastern part of the TP. In addition, The spatial and seasonal variability in the cloud fraction over the TP (Figure 4) may be induced by water vapor and aerosol loading conditions [50,102]. Besides, more ice clouds during the cold season may be linked with atmospheric temperature and microphysical properties (e.g., ice nuclei aerosol) [21,102].…”

Variability in projected elevation dependent warming in boreal midlatitude winter in CMIP5 climate models and its potential drivers

“…A critical open question is how water vapor will increase in the future, and in particular, how it will increase with elevation. Furthermore, both cloud cover and cloud optical thickness can influence d(ΔTmin)/dz through their effect on surface downward longwave radiation (DLR) (e.g., Naud et al 2014). However, Naud et al (2013) showed that the DLR-q relationship is not appreciably affected by the absence or presence of clouds.…”

Seasonal and Regional Variability of Long-Wave Effective Radiation in China and Associated Modulating Factors

“…e 1.0°× 1.0°gridded surface DLR and ULR are provided in SYN1deg Ed3A data. CERES surface fluxes are calculated using a radiative transfer model provided by the Global Modeling and Assimilation Office and MODIS-derived cloud and aerosol properties [23,29]; these calculations are then constrained and fixed by observed TOA outgoing fluxes [30].…”