Observational estimates of global top-of-atmosphere radiation on monthly, seasonal, annual, and longer time-scales require estimates of the diurnal variability in insolation and the asymmetry of surface and atmospheric reflection. We compare EPIC and NISTAR observations from the DSCOVR satellite with CERES hourly synoptic fluxes, which are filled through geostationary observations, and find that a Fourier analysis of these data substantially agree, showing strong relative power at sub-diurnal, diurnal, seasonal, and annual time-scales, and power growing from diurnal to seasonal time-scales. Frequency analysis of fluxes from several models shows that they distribute too much power over periods greater than 1 day but less than one year, indicating that a closer look is needed into how models achieve longer-term stability in reflected shortwave radiation. Model developers can consider using these datasets for time-varying energetic constraints, since tuning parameter choices will impact modeled planetary shortwave radiation across timescales ranging from sub-diurnal to decadal.

Uncertainty in Arctic top-of-atmosphere (TOA) radiative flux observations stems from the low sun angles and the heterogeneous scenes. Advancing our understanding of the Arctic climate system requires improved TOA radiative fluxes. We compare Cloud and Earth’s Radiant Energy System (CERES) TOA radiative fluxes with Arctic Radiation-IceBridge Sea and Ice Experiment (ARISE) airborne measurements using two approaches: grid box averages and instantaneously-matched footprints. Both approaches indicate excellent agreement in the longwave and good agreement in the shortwave, within 2 uncertainty considering all error sources (CERES and airborne radiometer calibration, inversion, and sampling). While the SW differences are within 2 uncertainty, both approaches show a ~‑10 W m‑2 average CERES-aircraft flux difference. Investigating the source of this negative difference, we find a substantial sensitivity of the flux differences to the sea ice concentration dataset. Switching from imager-based to passive microwave-based sea ice data in the CERES inversion process reduces the differences in the grid box average fluxes and in the sea ice partly cloudy scene anisotropy in the matched footprints. In the long-term, more accurate sea ice concentration data are needed to reduce CERES TOA SW flux uncertainties. Switching from imager to passive microwave sea ice data, in the short-term, could improve CERES TOA SW fluxes in polar regions, additional testing is required. Our analysis indicates that calibration and sampling uncertainty limit the ability to place strong constraints (<±7%) on CERES TOA fluxes with aircraft measurements.

Biases in aerosol optical depths (AOD) and land surface albedos in the AeroCom models are manifested in the top-of-atmosphere (TOA) clear-sky reflected shortwave (SW) fluxes. Biases in the SW fluxes from AeroCom models are quantitatively related to biases in AOD and land surface albedo by using their radiative kernels. Over ocean, AOD contributes about 25% to the 60°S-60°N mean SW flux bias for the multi-model mean (MMM) result. Over land, AOD and land surface albedo contribute about 40% and 30%, respectively, to the 60°S-60°N mean SW flux bias for the MMM result. Furthermore, the spatial patterns of the SW flux biases derived from the radiative kernels are very similar to those between models and CERES observation, with the correlation coefficient of 0.6 over ocean and 0.76 over land for MMM using data of 2010. Satellite data used in this evaluation are derived independently from each other, consistencies in their bias patterns when compared with model simulations suggest that these patterns are robust. This highlights the importance of evaluating related variables in a synergistic manner to provide an unambiguous assessment of the models, as results from single parameter assessments are often confounded by measurement uncertainty. We also compare the AOD trend from three models with the observation-based counterpart. These models reproduce all notable trends in AOD (i.e. decreasing trend over eastern United States and increasing trend over India) except the decreasing trend over eastern China and the adjacent oceanic regions due to limitations in the emission dataset.