OP-E1.6 Solar Radiation Benchmark
| Poster Title | Solar Radiation Benchmark Code for E3SM |
|---|---|
| Authors | Michael J Prather , Juno Hsu, Philip Cameron-Smith (Unlicensed) |
| First Author | Michael J Prather |
| Session Type | E3SM Session |
| Session ID | E1 |
| Submission Type | Presentation |
| Group | Atmosphere |
| Experiment | Watercycle, BGC, & Cryosphere |
| Poster Link |
Abstract
To follow the path of sunlight through the Earth system, one needs consider attenuation, scattering, absorption, refraction and reflection throughout the atmosphere, ocean, cryosphere, and land surface, plus the wavelength dependence of these radiative transfer (RT) processes. This RT problem includes gases, surfaces, and aerosols (clouds). Using Solar-J as a flexible RT code, we build a benchmark code for solar radiation using RT components that are more accurate than typical approximations used in the operational solar heating codes. Using a range of versions of Solar-J, we identify and evaluate the errors in RT approximations by integrating over realistic global atmospheric conditions taken from a weather forecast model. Solar-J was built on a photochemistry model at short wavelengths attached to the gas-phase absorption model from RRTMG-SW for wavelengths greater than 0.6 microns. In this study, we assess separately the errors in each of the following approximations with the goal of identifying which improvements are highest priority for the next generation of solar radiation modules in Earth system models:
- Geometry, where incident solar rays see a spherical Earth vs. a flat disk;
- Refraction, where the solar rays are bent and wrap past the terminator;
- Scattering, where the cloud and aerosol scattering is 8-stream forward-peaked vs. 2-stream isotropic with it greatly reduced optical depth for clouds;
- Cloud overlap, where vertical decorrelation length is used vs. maximum-random overlap;
- Column atmosphere sampling, where different cloud structures and wavelengths are integrated independently (Cloud-J) vs. random sampling across both of these dimensions (MCICA);
- Ocean albedo, where the ocean surface albedo varies with incident angle, wavelength, wind speed and chlorophyll;
- Water-vapor wavelength bins, CLIRAD vs. RRTMG;
- Cloud wavelength bins, where spectral properties of water clouds are resolved vs. averaged across super-bins.
We evaluate the errors in total solar energy (W m-2) reflected, absorbed in the atmosphere, and absorbed at the surface, in terms of mean and rms errors in each column atmosphere over 744 hours in January, but do not follow through with the impact on climate. There remain RT errors within the scope of Solar-J (e.g., aerosol direct radiative forcing, photosynthetically active radiation) and well beyond it (e.g., 3D RT and the interaction of neighboring grid cells).