Effective radiative forcing and adjustments in CMIP6 models

Abstract

Abstract. The effective radiative forcing, which includes the instantaneous forcing plus adjustments from the atmosphere and surface, has emerged as the key metric of evaluating human and natural influence on the climate. We evaluate effective radiative forcing and adjustments in 17 contemporary climate models that are participating in the Coupled Model Intercomparison Project (CMIP6) and have contributed to the Radiative Forcing Model Intercomparison Project (RFMIP). Present-day (2014) global-mean anthropogenic forcing relative to pre-industrial (1850) levels from climate models stands at 2.00 (±0.23) W m−2, comprised of 1.81 (±0.09) W m−2 from CO2, 1.08 (± 0.21) W m−2 from other well-mixed greenhouse gases, −1.01 (± 0.23) W m−2 from aerosols and −0.09 (±0.13) W m−2 from land use change. Quoted uncertainties are 1 standard deviation across model best estimates, and 90 % confidence in the reported forcings, due to internal variability, is typically within 0.1 W m−2. The majority of the remaining 0.21 W m−2 is likely to be from ozone. In most cases, the largest contributors to the spread in effective radiative forcing (ERF) is from the instantaneous radiative forcing (IRF) and from cloud responses, particularly aerosol–cloud interactions to aerosol forcing. As determined in previous studies, cancellation of tropospheric and surface adjustments means that the stratospherically adjusted radiative forcing is approximately equal to ERF for greenhouse gas forcing but not for aerosols, and consequentially, not for the anthropogenic total. The spread of aerosol forcing ranges from −0.63 to −1.37 W m−2, exhibiting a less negative mean and narrower range compared to 10 CMIP5 models. The spread in 4×CO2 forcing has also narrowed in CMIP6 compared to 13 CMIP5 models. Aerosol forcing is uncorrelated with climate sensitivity. Therefore, there is no evidence to suggest that the increasing spread in climate sensitivity in CMIP6 models, particularly related to high-sensitivity models, is a consequence of a stronger negative present-day aerosol forcing and little evidence that modelling groups are systematically tuning climate sensitivity or aerosol forcing to recreate observed historical warming.

Biases in the albedo sensitivity to deforestation in CMIP5 models and their impacts on the associated historical radiative forcing

“…In our case, this perturbation is a modification of surface albedo arising from land cover changes, in particular transitions between trees and cropsgrasses, which affects the amount of reflected shortwave radiation leaving the Earth system at the top of the atmosphere. By how much this amount changes depends on a so-called radiative kernel K α s (Soden et al, 2008), defined in this case as the differential response in outgoing shortwave radiation at the top of the atmosphere to an incremental change in surface albedo δα s (Bright and O'halloran, 2019):…”

The HadGEM3-GA7.1 radiative kernel: the importance of a well-resolved stratosphere

“…Figure 5 shows the stratospheric temperature adjustment for 4×CO 2 in 13 models contributing to RFMIP. A simplified tropopause definition is used here, borrowed from Soden et al (2008), of a linear-inlatitude ramp from 100 hPa at the Equator to 300 hPa at the poles. There is a spread of around 1 W m −2 in calculated stratospheric temperature adjustment for each model using the full range of kernels, which is about 13 % of the effective radiative forcing (ERF) for a quadrupling of CO 2 from these models (Smith et al, 2020).…”

“…This clear-sky residual definition is more common in the literature (Smith et al, 2018;Soden et al, 2008;Vial et al, 2013), although often the clear-sky IRF is estimated rather than calculated directly. However, in some circumstances, clear-sky and all-sky IRF are known to be identically zero (e.g.…”

“…Radiative kernels describe how a small change in an atmospheric state variable affects the Earth's energy balance (Soden et al, 2008;Shell et al, 2008). They allow an analysis of climate feedbacks (Shell et al, 2008;Soden et al, 2008;Sanderson and Shell, 2012;Jonko et al, 2012;Block and Mauritsen, 2013;Huang, 2013) or forcing adjustments (Vial et al, 2013;Zhang and Huang, 2014;Chung and Soden, 2015b;Smith et al, 2018Smith et al, , 2020Myhre et al, 2018) from standardised climate model diagnostics such as those from the Coupled Model Intercomparison Projects (CMIPs). The use of radiative kernels is efficient, removing the need for time-and memory-consuming calculations of climate feedbacks online through partial radiative perturbation calculations (Wetherald and Manabe, 1988) or offline using a stand-alone version of the model radiative transfer code (Colman and McAvaney, 2011).…”

“…Cloud adjustments and feedbacks cannot be determined directly using atmospheric state kernels. They may be diagnosed using the cloud kernel based on International Satellite Cloud Climatology Project (ISCCP) simulator diagnostics (Zelinka et al, 2012) or from the residual of all-sky and clear-sky radiative kernels (Soden et al, 2008;Shell et al, 2008). For adjustments this calculation is…”