Tracing the physical signatures among the calculated global clear-sky spectral shortwave radiative flux distribution

This study utilized the high-spectral resolution radiative transfer model (MODerate resolution atmospheric TRANsmission, MODTRAN6.0.2.5) to compute global clear-sky shortwave (SW) radiative flux and compared it with NASA’s Clouds and the Earth’s Radiant Energy System (CERES) Synoptic Radiative Fluxes and Clouds (SYN1deg) product. The comparison revealed that the global distributions of clear-sky downwelling SW fluxes at the surface from the M6.0 calculations and SYN1 results are similar, with annual means of 246.51 Wm^-2 and 242.42 Wm^-2, respectively. Analysis further showed that most of the M6.0 calculations are slightly higher from low to mid-latitudes, particularly in the Northern Hemisphere (NH), but lower in higher latitudes compared to SYN1 results. However, these differences mostly fall within the CERES estimated uncertainty (6 Wm^-2) of monthly mean clear-sky downwelling SW flux at the surface. The sensitivity of clear-sky SW/${\mu }_0$ fluxes to changes in Precipitable Water Vapor (PWV), represented by the clear-sky water vapor radiative kernel, is about -0.7 Wm^-2/(kgm^-2) over oceans for both M6.0 and CERES SYN1 products, except for SYN1 results over the Southern Hemisphere (SH) ocean. Additionally, the zonal means of land coverage and SW/VIS/NIR albedos from M6.0 calculations indicate that VIS albedos are highest in polar regions ($>$60°), followed by SW and NIR albedos, while NIR albedos become highest from low to mid-latitudes ($<$60°). Generally, SW/VIS/NIR albedos and their differences increase monotonically with increased land coverage from 60°S to 60°N. The consistent clear-sky water vapor radiative kernels derived from both products exceeded our expectations, suggesting their potential use to trace physical signatures in climate model calculations. It is recommended that these model-derived radiative kernels should be validated by the long-term global and regional surface observations in order to enhance confidence to implement these radiative kernels in climate models.