Benchmark Calculations of Radiative Forcing by Greenhouse Gases

Abstract

Changes in concentrations of greenhouse gases lead to changes in radiative fluxes throughout the atmosphere. The value of this change, the instantaneous radiative forcing, varies across climate models, due partly to differences in the distribution of clouds, humidity, and temperature across models and partly due to errors introduced by approximate treatments of radiative transfer. This paper describes an experiment within the Radiative Forcing Model Intercomparision Project that uses benchmark calculations made with line-by-line models to identify parameterization error in the representation of absorption and emission by greenhouse gases. Clear-sky instantaneous forcing by greenhouse gases is computed using a set of 100 profiles, selected from a reanalysis of present-day conditions, that represent the global annual mean forcing from preindustrial times to the present day with sampling errors of less than 0.01 W m −2. Six contributing line-by-line models agree in their estimate of this forcing to within 0.025 W m −2 while even recently developed parameterizations have typical errors 4 or more times larger, suggesting both that the samples reveal true differences among line-by-line models and that parameterization error will be readily identifiable. Agreement among line-by-line models is better in the longwave than in the shortwave where differing treatments of the water vapor continuum affect estimates of forcing by carbon dioxide and methane. The impacts of clouds on instantaneous radiative forcing are estimated from climate model simulations, and the adjustment due to stratospheric temperature changes estimated by assuming fixed dynamical heating. Adjustments are large only for ozone and for carbon dioxide, for which stratospheric cooling introduces modest nonlinearity. The models participating in the previous phase of CMIP translated prescribed changes in atmospheric composition into a relatively wide range of effective radiative forcing, much of which remains even when model-specific adjustments are accounted for (e.g., Chung & Soden, 2015); initial results (Smith et al., 2020

Temperature‐Dependence of the Clear‐Sky Feedback in Radiative‐Convective Equilibrium

“…RRTMG is a fast radiation scheme which uses the correlated-k method with precalculated lookup tables for computational efficiency. For line-by-line simulations we replace the RRTMG longwave radiative transfer calculations with calculations using the Atmospheric Radiative Transfer Simulator (ARTS) (Buehler et al, 2018;Eriksson et al, 2011) which has been part of the Radiative Forcing Model Intercomparison Project (RFMIP, Pincus et al, 2020). In the chosen setup, ARTS represents the longwave radiative fluxes based on 32768 equidistant frequency points between 10 cm −1 and 3250 cm −1 (Δν = 0.1 cm −1 ).…”

Apportionment of the Pre‐Industrial to Present‐Day Climate Forcing by Methane Using UKESM1: The Role of the Cloud Radiative Effect

“…Nevertheless, the results indicate that the direct CH 4 ERF is better represented in UKESM1 than in HadGEM2 and is more consistent with the Etminan et al ( 2016) expression based on line-by-line radiative transfer calculations. UKESM1 is also more consistent with the multi-model mean of the IRF due to a PD-to-PI CH 4 perturbation from present day conditions in Pincus et al (2020). This improvement in UKESM1 is three-fold: (a) the inclusion of SW absorption by CH 4 , (b) the update to the LW spectral data for CH 4 , and (c) the absence of an anomalous dust response in the UKESM1 CH 4 perturbation experiments.…”

Accelerating Radiation Computations for Dynamical Models With Targeted Machine Learning and Code Optimization

“…computed by LBLRTM 12.8 (Clough et al, 2005), the model on which RRTMGP was trained, obtaining results for the RFMIP examples from the Earth System Grid (Pincus et al, 2020). Heating rate errors averaged over 15 RFMIP profiles set aside for testing are first shown in Figure 2 for three different experiments.…”