Effective radiative forcing and adjustments in CMIP6 models

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 13 contemporary climate models that are participating in CMIP6 and have contributed to the Radiative Forcing Model Intercomparison Project (RFMIP). Present-day (2014) global mean anthropogenic forcing relative to pre-industrial (1850) from climate models stands at 1.97 (± 0.26) W m−2, comprised of 1.80 (± 0.11) W m−2 from CO2, 1.07 (± 0.21) W m−2 from other well-mixed greenhouse gases, −1.04 (± 0.23) W m−2 from aerosols and −0.08 (± 0.14) W m−2 from land use change. Quoted uncertainties are one 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 0.17 W m−2 is likely to be from ozone. As determined in previous studies, cancellation of tropospheric and surface adjustments means that the traditional 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 equilibrium 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

Local and remote temperature response of regional SO₂ emissions

Short-lived anthropogenic climate forcers (SLCFs), such as sulfate aerosols, affect both climate and air quality. Despite being short-lived, these forcers do not affect temperatures only locally; regions far away from the emission sources are also affected. Climate metrics are often used in a policy context to compare the climate impact of different anthropogenic forcing agents. These metrics typically relate a forcing change in a certain region with a temperature change in another region and thus often require a separate model to convert emission changes to radiative forcing (RF) changes. In this study, we used a coupled Earth system model, NorESM (Norwegian Earth System Model), to calculate emission-to-temperature-response metrics for sulfur dioxide (SO2) emission changes in four different policy-relevant regions: Europe (EU), North America (NA), East Asia (EA) and South Asia (SA). We first increased the SO2 emissions in each individual region by an amount giving approximately the same global average radiative forcing change (−0.45&thinsp;Wm−2). The global mean temperature change per unit sulfur emission compared to the control experiment was independent of emission region and equal to ∼0.006&thinsp;K(TgSyr−1)−1. On a regional scale, the Arctic showed the largest temperature response in all experiments. The second largest temperature change occurred in the region of the imposed emission increase, except when South Asian emissions were changed; in this experiment, the temperature response was approximately the same in South Asia and East Asia. We also examined the non-linearity of the temperature response by removing all anthropogenic SO2 emissions over Europe in one experiment. In this case, the temperature response (both global and regional) was twice that in the corresponding experiment with a European emission increase. This non-linearity in the temperature response is one of many uncertainties associated with the use of simplified climate metrics.</p

A production-tagged aerosol module for Earth system models, OsloAero5.3-extensions and updates for CAM5.3-Oslo

We document model updates and present and discuss modeling and validation results from a further developed production-tagged aerosol module, OsloAero5.3, for use in Earth system models. The aerosol module has in this study been implemented and applied in CAM5.3-Oslo. This model is based on CAM5.3-CESM1.2 and its own predecessor model version CAM4-Oslo. OsloAero5.3 has improved treatment of emissions, aerosol chemistry, particle life cycle, and aerosol-cloud interactions compared to its predecessor OsloAero4.0 in CAM4-Oslo. The main new features consist of improved aerosol sources; the module now explicitly accounts for aerosol particle nucleation and secondary organic aerosol production, with new emissions schemes also for sea salt, dimethyl sulfide (DMS), and marine primary organics. Mineral dust emissions are updated as well, adopting the formulation of CESM1.2. The improved model representation of aerosol-cloud interactions now resolves heterogeneous ice nucleation based on black carbon (BC) and mineral dust calculated by the model and treats the activation of cloud condensation nuclei (CCN) as in CAM5.3. Compared to OsloAero4.0 in CAM4-Oslo, the black carbon (BC) mass concentrations are less excessive aloft, with a better fit to observations. Near-surface mass concentrations of BC and sea salt aerosols are also less biased, while sulfate and mineral dust are slightly more biased. Although appearing quite similar for CAM5.3-Oslo and CAM4-Oslo, the validation results for organic matter (OM) are inconclusive, since both of the respective versions of OsloAero are equipped with a limited number of OM tracers for the sake of computational efficiency. Any information about the assumed mass ratios of OM to organic carbon (OC) for different types of OM sources is lost in the transport module. Assuming that observed OC concentrations scaled by 1.4 are representative for the modeled OM concentrations, CAM5.3-Oslo with OsloAero5.3 is slightly inferior for the very sparsely available observation data. Comparing clear-sky column-integrated optical properties with data from ground-based remote sensing, we find a negative bias in optical depth globally; however, it is not as strong as in CAM4-Oslo, but has positive biases in some areas typically dominated by mineral dust emissions. Aerosol absorption has a larger negative bias than the optical depth globally. This is reflected in a lower positive bias in areas where mineral dust is the main contributor to absorption. Globally, the low bias in absorption is smaller than in CAM4-Oslo. The Angstrom parameter exhibits small biases both globally and regionally, suggesting that the aerosol particle sizes are reasonably well represented. Cloud-top droplet number concentrations over oceans are generally underestimated compared to satellite retrievals, but seem to be overestimated downwind of major emissions of dust and biomass burning sources. Finally, we find small changes in direct radiative forcing at the top of the atmosphere, while the cloud radiative forcing due to anthropogenic aerosols is now more negative than in CAM4-Oslo, being on the strong side compared to the multi-model estimate in IPCC AR5. Although not all validation results in this study show improvement for the present CAM5.3-Oslo version, the extended and updated aerosol module OsloAero5.3 is more advanced and applicable than its predecessor OsloAero4.0, as it includes new parameterizations that more readily facilitate sensitivity and process studies and use in climate and Earth system model studies in general.Peer reviewe