Decomposing Shortwave Top-of-Atmosphere and Surface Radiative Flux Variations in Terms of Surface and Atmospheric Contributions

Abstract

A diagnostic tool for determining surface and atmospheric contributions to interannual variations in top-of-atmosphere (TOA) reflected shortwave (SW) and net downward SW surface radiative fluxes is introduced. The method requires only upward and downward radiative fluxes at the TOA and surface as input and therefore can readily be applied to both satellite-derived and model-generated radiative fluxes. Observations from the Clouds and the Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) Edition 4.0 product show that 81% of the monthly variability in global mean reflected SW TOA flux anomalies is associated with atmospheric variations (mainly clouds), 6% is from surface variations, and 13% is from atmosphere–surface covariability. Over the Arctic Ocean, most of the variability in both reflected SW TOA flux and net downward SW surface flux anomalies is explained by variations in sea ice and cloud fraction alone (r2 = 0.94). Compared to CERES, variability in two reanalyses—the ECMWF interim reanalysis (ERA-Interim) and NASA’s Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2)—show large differences in the regional distribution of variance for both the atmospheric and surface contributions to anomalies in net downward SW surface flux. For MERRA-2 the atmospheric contribution is 17% too large compared to CERES while ERA-Interim underestimates the variance by 15%. The difference is mainly due to how cloud variations are represented in the reanalyses. The overall surface contribution in both ERA-Interim and MERRA-2 is smaller than CERES EBAF by 15% for ERA-Interim and 58% for MERRA-2, highlighting limitations of the reanalyses in representing surface albedo variations and their influence on SW radiative fluxes.

CERES MODIS Cloud Product Retrievals for Edition 4—Part I: Algorithm Changes

“…MCAT-ML upper and lower layer cloud optical depth. τ 4 , τ 12 Cloud optical depth for channels 4 and 12.…”

“…was applied within the cloud retrieval framework. In (12), ε s4 is the IRW surface emissivity, and B 4 and B −1 4 are the IRW Planck function and its inverse, respectively. The atmospheric attenuation and contribution factor Tr is computed as in [89, eq.…”

“…The values for ε s4 are the same as those used in [29] and [40]. Omission of the surface reflectance term (1 − ε s4 ) in (12) will cause some overestimation of T s , particularly for low-emissivity surfaces.…”

“…These measurements and their conversion to physical parameters are designed to create a long-term climate data set for monitoring the radiation budget and for exploring the relationships among clouds, aerosols, radiation, and various surface and atmospheric variables (see [4]- [9]). As they are valuable for tracking climate parameters (see [10]- [12]), for improving our understanding of climate (see [13]- [19]), assessing climate model output (see [20]- [23]), and other applications (see [24]), the CERES data are occasionally reanalyzed with the latest calibrations and methods to increase their accuracy and consistency.…”

Changes in Clear‐Sky Shortwave Aerosol Direct Radiative Effects Since 2002

“…The second term accounts for variations in α for a climatological atmosphere. To determine the atmospheric and surface contributions, we use the Decomposition Method described in Loeb et al (2019). Substituting Equation 3 into Equation 2 yields:…”