Freidenreich, Stuart, David J Paynter, Pu Lin, V Ramaswamy, Alexandra L Jones, Daniel Feldman, and William D Collins, June 2021: An investigation into biases in instantaneous aerosol radiative effects calculated by shortwave parameterizations in two Earth system models. JGR Atmospheres, 126(11), DOI:10.1029/2019JD032323. Abstract
Because the forcings to which Coupled Model Intercomparison Project - Phase 5 (CMIP5) models were subjected were poorly quantified, recent efforts from the Radiative Forcing Model Intercomparison Project (RFMIP) have focused on developing and testing models with exacting benchmarks. Here, we focus on aerosol forcing to understand if for a given distribution of aerosols, participating models are producing a radiometrically-accurate forcing. We apply the RFMIP experimental protocol for assessing flux biases in aerosol instantaneous radiative effect (IRE) on two participating models, GFDL AM4 and CESM 1.2.2. The latter model contains the RRTMG radiation code which is widely used among CMIP6 GCM's. We conduct a series of calculations that test different potential sources of error in these models relative to line-by-line benchmarks. We find two primary sources of error: two-stream solution methods and the techniques to resolve spectral dependencies of absorption and scattering across the solar spectrum. The former is the dominant source of error for both models but the latter is more significant as a contributing factor for CESM 1.2.2. Either source of error can be addressed in future model development, and these results both demonstrate how the RFMIP protocol can help determine the origins of parameterized errors relative to their equivalent benchmark calculations for participating models, as well as highlight a viable path towards a more rigorous quantification and control of forcings for future CMIP exercises.
Jones, Alexandra L., and L Di Girolamo, March 2018: Design and Verification of a New Monochromatic Thermal Emission Component for the I3RC Community Monte Carlo Model. Journal of the Atmospheric Sciences, 75(3), DOI:10.1175/JAS-D-17-0251.1. Abstract
The Intercomparison of 3D Radiation Codes (I3RC) community 3D Monte Carlo model has been extended to included a source of photon emission from the surface and atmosphere, thereby making it capable of simulating scalar radiative transfer in a 3D scattering, absorbing and emitting domain with both internal and external sources. The theoretical basis, computational implementation, verification of the internal emission, and computational performance of the resulting model, the “IMC+emission”, is presented. Thorough verification includes fundamental tests of reciprocity and energy conservation, comparison to analytical solutions, and comparison with another 3D model, SHDOM. All comparisons to fundamental tests and analytical solutions are accurate to within the precision of the simulations—typically better than 0.05%. Comparison cases to SHDOM were typically within a few percent, except for flux divergence near cloud edges where the effects grid definition between the two models manifest themselves. Finally, the model is applied to the established I3RC Case 4 cumulus cloud field to provide a benchmark result and computational performance and strong and weak scaling metrics are presented. The outcome is a thoroughly vetted, publicly available, open source, benchmark tool to study 3D radiative transfer from either internal or external sources of radiation at wavelengths for which scattering, emission, and absorption are important.
A new paradigm in benchmark absorption-scattering radiative transfer is presented that enables both the globally-averaged and spatially-resolved testing of climate model radiation parameterizations in order to uncover persistent sources of biases in the aerosol Instantaneous Radiative Effect (IRE). A proof-of-concept is demonstrated with the GFDL AM4 and CESM 1.2.2 climate models. Instead of prescribing atmospheric conditions and aerosols, as in prior intercomparisons, native snapshots of the atmospheric state and aerosol optical properties from the participating models are used as inputs to an accurate radiation solver to uncover model-relevant biases. These diagnostic results show that the models’ aerosol IRE bias is of the same magnitude as the persistent range cited (~1 W/m2), and also varies spatially and with intrinsic aerosol optical properties. The findings underscore the significance of native model error analysis and its dispositive ability to diagnose global biases, confirming its fundamental value for the Radiative Forcing Model Intercomparison Project.
