In calculating solar radiation, climate models make many simplifications, in part to reduce computational cost and enable climate modeling, and in part from lack of understanding of critical atmospheric information. Whether known errors or unknown errors, the community's concern is how these could impact the modeled climate. The simplifications are well known and most have published studies evaluating them, but with individual studies it is difficult to compare. Here, we collect a wide range of such simplifications in either radiative transfer modeling or atmospheric conditions and assess potential errors within a consistent framework on climate-relevant scales. We build benchmarking capability around a solar heating code (Solar-J) that doubles as a photolysis code for chemistry and can be readily adapted to consider other errors and uncertainties. The broad classes here include: use of broad wavelength bands to integrate over spectral features; scattering approximations that alter phase function and optical depths for clouds and gases; uncertainty in ice-cloud optics; treatment of fractional cloud cover including overlap; and variability of ocean surface albedo. We geographically map the errors approximations assessed here, mean errors are ∼2 W m−2 with greater latitudinal biases and are likely to in W m−2 using a full climate re-creation for January 2015 from a weather forecasting model. For many affect a model’s ability to match the current climate state. Combining this work with previous studies, we make priority recommendations for fixing these simplifications based on both the magnitude of error and the ease or computational cost of the fix.
Plain Language Summary Solar heating of the climate system—the atmosphere, land surface, and ocean—drives the climate. Accurate numerical calculation of solar heating is a core component of the models we use to project and prepare for climate change. The community has identified many potential sources of error and published studies showing how to improve the solar heating codes used in climate models. Here, we assemble a wide range of these errors, either numerical approximations or uncertainties in defining atmospheric conditions, and put them through the same test: calculating the atmospheric and surface heating over a month of simulated climate conditions. Combining the new calculations here with previous work, we discuss more than a dozen specific areas where improvements could be made and identify high-priority actions.