Related YSE Profiles
Although the partitioning of shortwave radiation K-down arrow at the surface into its diffuse (K-down arrow,K-d) and direct beam (K-down arrow,K-b) components is relevant for, among other things, the terrestrial energy and carbon budgets, there is a dearth of large-scale comparisons of this partitioning across reanalysis and satellite-derived products. Here we evaluate K-down arrow, K-down arrow,K-d, and K-down arrow,K-b, as well as the diffuse fraction k(d) of solar radiation in four current-generation reanalysis datasets (NOAA-CIRES-DOE, NCEP-NCAR, MERRA-2, and ERA5) and one satellite-derived product (CERES) using approximate to 1400 site-years of observations. Although the systematic positive biases in K-down arrow are consistent with previous studies, the biases in gridded K-down arrow,K-d and K-down arrow,K-b vary in direction and magnitude, both annually and across seasons. The intermodel variability in cloud cover strongly explains the biases in both K-down arrow,K-d and K-down arrow,K-b. Over Europe and China, the long-term (10 yr and longer) trends in K-down arrow,K-d in the gridded products differ noticeably from corresponding observations and the grid-averaged 35-yr trends show an order of magnitude variability. In the MERRA-2 reanalysis, which includes both clouds and assimilated aerosols, the reductions in both clouds and aerosols reinforce each other to establish brightening trends over Europe, whereas the effect of increasing aerosols overwhelms the effect of decreasing cloud cover over China. The intermodel variability in k(d) seen here (from 0.27 to 0.50 from CERES to MERRA-2) suggests substantial differences in shortwave parameterization schemes and their inputs in climate models and can contribute to intermodel variability in coupled simulations. From these results, we call for systematic evaluations of K-down arrow,K-d and K-down arrow,K-b in CMIP6 models. SIGNIFICANCE STATEMENT The direction of sunlight can be changed by particles and clouds in the air. This is known as diffuse light, and it affects solar energy generation and plant growth. Here, we address a gap in previous studies and compare the diffuse light in global datasets. We find large differences between datasets, explained mostly by differing cloud amounts. When compared with measurements from the ground, we find that these differences exist for most sites and across seasons. The change in diffuse light over the last 35 years also varies widely among datasets. Our results call for larger-scale comparisons of diffuse light in all current-generation global models. Doing so can help us to better constrain future climate change.