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.

A new radiation package, "McRad," has become operational with cycle 32R2 of the Integrated Forecasting System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF). McRad includes an improved description of the land surface albedo from Moderate Resolution Imaging Spectroradiometer (MODIS) observations, the Monte Carlo independent column approximation treatment of the radiative transfer in clouds, and the Rapid Radiative Transfer Model shortwave scheme. The impact of McRad on year-long simulations at T L 159L91 and higher-resolution 10-day forecasts is then documented.McRad is shown to benefit the representation of most parameters over both shorter and longer time scales, relative to the previous operational version of the radiative transfer schemes. At all resolutions, McRad improves the representation of the cloud-radiation interactions, particularly in the tropical regions, with improved temperature and wind objective scores through a reduction of some systematic errors in the position of tropical convection as a result of a change in the overall distribution of diabatic heating over the vertical plane, inducing a geographical redistribution of the centers of convection. Although smaller, the improvement is also seen in the rmse of geopotential in the Northern and Southern Hemispheres and over Europe. Given the importance of cloudiness in modulating the radiative fluxes, the sensitivity of the model to cloud overlap assumption (COA) is also addressed, with emphasis on the flexibility that is inherent to this new RT approach when dealing with COA. The sensitivity of the forecasts to the space interpolation that is required to efficiently address the high computational cost of the RT parameterization is also revisited. A reduction of the radiation grid for the Ensemble Prediction System is shown to be of little impact on the scores while reducing the computational cost of the radiation computations. McRad is also shown to decrease the cold bias in ocean surface temperature in climate integrations with a coupled ocean system.

[1] We outline a methodology for estimating fractional sky cover for an effective 160°f ield of view from an analysis of surface measurements of downwelling total and diffuse shortwave (SW) irradiance. The data are screened for optically thicker overcast cases, after which an empirically derived formulation is used to estimate the fractional sky cover for the remaining data. The retrieved fractional sky cover time series is then evaluated to mitigate times of anomalous behavior caused by the thick overcast screening. The resultant sky cover estimates show a high degree of repeatability given nominally well maintained and operated radiometer systems and the use of the Long and Ackerman (2000) methodology for estimating the clear-sky total and diffuse SW. Thus the resultant fractional sky-cover estimates appear to be fairly independent of the particular climate regime and model of radiometers used, at least for the climate regimes we have tested so far. The sky-cover estimates agree to better than 10% root mean square sky cover amount with sky imager retrievals and human observations, which is as good as the agreement between sky imaging systems and observers themselves. As such, this methodology becomes a powerful tool for satellite and model validations and climatological analyses including the study of trends in cloud amount. Analysis shows that the technique also produces realistic frequency distributions, showing that the continental midlatitude regimes included in the study are typified by clear-sky occurring about 1/3 of the time, overcast about 1/3 of the time, and partly cloudy skies to varying extent occurring the remaining 1/3 of the time. By contrast, the tropical western Pacific oceanic regime during the Nauru99 field experiment exhibits far more frequent occurrence of partly cloudy skies, with sky cover amounts of 20% to 50% occurring about half the time.Citation: Long, C. N., T. P. Ackerman, K. L. Gaustad, and J. N. S. Cole (2006), Estimation of fractional sky cover from broadband shortwave radiometer measurements,

ABSTRACT:The Monte Carlo Independent Column Approximation (McICA) computes domain-average, broadband radiative flux profiles within conventional global climate models (GCMs). While McICA is unbiased with respect to the full ICA, it generates, as a by-product, random noise. If this by-product leads to statistically significant impacts on GCM simulations, it could limit the usefulness of McICA. This paper assesses the impact of McICA's random noise on six GCMs. To this end, the GCMs performed ensembles of 14-day long simulations for various renditions of McICA, each with differing amounts of random noise. As seen in the past, low-cloud fraction and surface temperature were affected most by noise. However, all GCM simulations using operationally viable renditions of McICA showed no statistically significant impacts, even for precipitation -a highly intermittent variable that one might expect to be sensitive to random fluctuations. Two GCMs showed statistically significant responses using an academic version of McICA that generates overly large sampling noise. Time series analyses of high-resolution (i.e. typically 2-hourly) data revealed that fluctuations associated with most variables and GCMs are immune to McICA noise. Moreover, the nature of these fluctuations can vary substantially among GCMs and most often they overwhelm any noise impacts. Overall, the results presented here corroborate a range of previous studies done on one GCM at a time: random noise produced by recommended versions of McICA has statistically insignificant effects on GCM simulations.

Forcing due to solar and volcanic variability, on the natural side, and greenhouse gas and aerosol emissions, on the anthropogenic side, are the main inputs to climate models. Reliable climate model simulations of past and future climate change depend crucially upon them. Here we analyze large ensembles of simulations using a comprehensive Earth System Model to quantify uncertainties in global climate change attributable to differences in prescribed forcings. The different forcings considered here are those used in the two most recent phases of the Coupled Model Intercomparison Project (CMIP), namely CMIP5 and CMIP6. We show significant differences in simulated global surface air temperature due to volcanic aerosol forcing in the second half of the 19th century and in the early 21st century. The latter arise from small-to-moderate eruptions incorporated in CMIP6 simulations but not in CMIP5 simulations. We also find significant differences in global surface air temperature and Arctic sea ice area due to anthropogenic aerosol forcing in the second half of the 20th century and early 21st century. These differences are as large as those obtained in different versions of an Earth System Model employing identical forcings. In simulations from 2015 to 2100, we find significant differences in the rates of projected global warming arising from CMIP5 and CMIP6 concentration pathways that differ slightly but are equivalent in terms of their nominal radiative forcing levels in 2100. Our results highlight the influence of assumptions about natural and anthropogenic aerosol loadings on carbon budgets, the likelihood of meeting Paris targets, and the equivalence of future forcing scenarios.