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Aerosols can cause brightening of stratocumulus clouds, thereby cooling the climate. Observations and models disagree on the magnitude of this cooling, partly because of the aerosol-induced liquid water path (LWP) adjustment, with climate models predicting an increase in the LWP and satellites observing a weak decrease in response to increasing aerosols. With higher-resolution global climate models, which allow the simulation of mesoscale circulations in which stratocumulus clouds are embedded, there is hope to start bridging this gap. In this study, we present boreal summertime simulations conducted with the ICOsahedral Non-hydrostatic (ICON) global storm-resolving model (GSRM). Compared to geostationary satellite data, ICON produces realistic cloud coverage in the stratocumulus regions; however, these clouds look cumuliform, and the sign of LWP adjustments disagrees with observations. We investigate this disagreement with a causal approach, which combines time series with knowledge of cloud processes, allowing us to diagnose the sources of observation–model discrepancies. The positive ICON LWP adjustment results from a superposition of processes, with an overestimated positive response due to (1) precipitation suppression, (2) a lack of wet scavenging, and (3) cloud deepening under a weak inversion, despite (4) small negative influences from cloud-top entrainment enhancement. We also find that precipitation suppression and entrainment enhancement occur at different intensities during the day and the night, implying that daytime satellite studies suffer from selection bias. This causal methodology can guide modelers on how to modify model parameterizations and setups to reconcile conflicting studies concerning the sign and magnitude of LWP adjustments across different data sources.

Anthropogenic aerosols play a major role in the Earth–atmosphere system by influencing the Earth's radiative budget and precipitation and consequently the climate. The perturbation induced by changes in anthropogenic aerosols on the Earth's energy balance is quantified in terms of the effective radiative forcing (ERF). In this work, the present-day shortwave (SW), longwave (LW), and total (i.e., SW plus LW) ERF of anthropogenic aerosols is quantified using two different sets of experiments with prescribed sea surface temperatures (SSTs) from Earth system models (ESMs) participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6): (a) time-slice pre-industrial perturbation simulations with fixed SSTs (piClim) and (b) transient historical simulations with time-evolving SSTs (histSST) over the historical period (1850–2014). ERF is decomposed into three components for both piClim and histSST experiments: (a) ERFARI, representing aerosol–radiation interactions; (b) ERFACI, accounting for aerosol–cloud interactions (including the semi-direct effect); and (c) ERFALB, which is due to temperature, humidity, and surface albedo changes caused by anthropogenic aerosols. We present spatial patterns at the top-of-atmosphere (TOA) and global weighted field means along with inter-model variability (1 standard deviation) for all SW, LW, and total ERF components (ERFARI, ERFACI, and ERFALB) and for every experiment used in this study. Moreover, the inter-model agreement and the robustness of our results are assessed using a comprehensive method as utilized in the IPCC Sixth Assessment Report. Based on piClim experiments, the total present-day (2014) ERF from anthropogenic aerosol and precursor emissions is estimated to be −1.11 ± 0.26 W m−2, mostly due to the large contribution of ERFACI to the global mean and to the inter-model variability. Based on the histSST experiments for the present-day period (1995–2014), similar results are derived, with a global mean total aerosol ERF of −1.28 ± 0.37 W m−2 and dominating contributions from ERFACI. The spatial patterns for total ERF and its components are similar in both the piClim and histSST experiments. Furthermore, implementing a novel approach to determine geographically the driving factor of ERF, we show that ERFACI dominates over the largest part of the Earth and that ERFALB dominates mainly over the poles, while ERFARI dominates over certain reflective surfaces. Analysis of the inter-model variability in total aerosol ERF shows that SW ERFACI is the main source of uncertainty predominantly over land regions with significant changes in aerosol optical depth (AOD), with eastern Asia contributing mostly to the inter-model spread of both ERFARI and ERFACI. The global spatial patterns of total ERF and its components from individual aerosol species, such as sulfates, organic carbon (OC), and black carbon (BC), are also calculated based on piClim experiments. The total ERF caused by sulfates (piClim-SO2) is estimated at −1.11 ± 0.31 W m−2, and the OC ERF (piClim-OC) is −0.35 ± 0.21 W m−2, while the ERF due to BC (piClim-BC) is 0.19 ± 0.18 W m−2. For sulfates and OC perturbation experiments, ERFACI dominates over the globe, whereas for BC perturbation experiments ERFARI dominates over land in the Northern Hemisphere and especially in the Arctic. Generally, sulfates dominate ERF spatial patterns, exerting a strongly negative ERF especially over industrialized regions of the Northern Hemisphere (NH), such as North America, Europe, and eastern and southern Asia. Our analysis of the temporal evolution of ERF over the historical period (1850–2014) reveals that ERFACI clearly dominates over ERFARI and ERFALB for driving the total ERF temporal evolution. Moreover, since the mid-1980s, total ERF has become less negative over eastern North America and western and central Europe, while over eastern and southern Asia there is a steady increase in ERF magnitude towards more negative values until 2014.

General circulation models' (GCMs) estimates of the liquid water path adjustment to anthropogenic aerosol emissions differ in sign from other lines of evidence. This reduces confidence in estimates of the effective radiative forcing of the climate by aerosol–cloud interactions (ERFaci). The discrepancy is thought to stem in part from GCMs' inability to represent the turbulence–microphysics interactions in cloud-top entrainment, a mechanism that leads to a reduction in liquid water in response to an anthropogenic increase in aerosols. In the real atmosphere, enhanced cloud-top entrainment is thought to be the dominant adjustment mechanism for liquid water path, weakening the overall ERFaci. We show that the latest generation of GCMs includes models that produce a negative correlation between the present-day cloud droplet number and liquid water path, a key piece of observational evidence supporting liquid water path reduction by anthropogenic aerosols and one that earlier-generation GCMs could not reproduce. However, even in GCMs with this negative correlation, the increase in anthropogenic aerosols from preindustrial to present-day values still leads to an increase in the simulated liquid water path due to the parameterized precipitation suppression mechanism. This adds to the evidence that correlations in the present-day climate are not necessarily causal. We investigate sources of confounding to explain the noncausal correlation between liquid water path and droplet number. These results are a reminder that assessments of climate parameters based on multiple lines of evidence must carefully consider the complementary strengths of different lines when the lines disagree.

Earth system models (ESMs) participating in the latest Coupled Model Intercomparison Project Phase 6 (CMIP6) simulate various components of fine particulate matter (PM2.5) as major climate forcers. Yet the model performance for PM2.5 components remains little evaluated due in part to a lack of observational data. Here, we evaluate near-surface concentrations of PM2.5 and its five main components over China as simulated by 14 CMIP6 models, including organic carbon (OC; available in 14 models), black carbon (BC; 14 models), sulfate (14 models), nitrate (4 models), and ammonium (5 models). For this purpose, we collect observational data between 2000 and 2014 from a satellite-based dataset for total PM2.5 and from 2469 measurement records in the literature for PM2.5 components. Seven models output total PM2.5 concentrations, and they all underestimate the observed total PM2.5 over eastern China, with GFDL-ESM4 (−1.5 %) and MPI-ESM-1-2-HAM (−1.1 %) exhibiting the smallest biases averaged over the whole country. The other seven models, for which we recalculate total PM2.5 from the available component output, underestimate the total PM2.5 concentrations partly because of the missing model representations of nitrate and ammonium. Concentrations of the five individual components are underestimated in almost all models, except that sulfate is overestimated in MPI-ESM-1-2-HAM by 12.6 % and in MRI-ESM2-0 by 24.5 %. The underestimation is the largest for OC (by −71.2 % to −37.8 % across the 14 models) and the smallest for BC (−47.9 % to −12.1 %). The multi-model mean (MMM) reproduces the observed spatial pattern for OC (R = 0.51), sulfate (R = 0.57), nitrate (R = 0.70) and ammonium (R = 0.74) fairly well, yet the agreement is poorer for BC (R = 0.39). The varying performances of ESMs on total PM2.5 and its components have important implications for the modeled magnitude and spatial pattern of aerosol radiative forcing.

The ice phase impacts many cloud properties as well as cloud lifetime. Ice particles that sediment into a lower cloud from an upper cloud (external seeder–feeder process) or into the mixed-phase region of a deep cloud from cirrus levels (internal seeder–feeder process) can influence the ice phase of the lower cloud, amplify cloud glaciation and enhance surface precipitation. Recently, numerical weather prediction modeling studies have aimed at representing the ice crystal number concentration in mixed-phase clouds more accurately by including secondary ice formation processes. The increase in the ice crystal number concentration can impact the number of ice particles that sediment into the lower cloud and alter its composition and precipitation formation. In the Swiss Alps, the orography permits the formation of orographic clouds, making it ideal for studying the occurrence of multi-layered clouds and the seeder–feeder process. We present results from a case study on 18 May 2016, showing the occurrence frequency of multi-layered clouds and the seeder–feeder process. About half of all observed clouds were categorized as multi-layered, and the external seeder–feeder process occurred in 10 % of these clouds. Between cloud layers, ≈60 % of the ice particle mass was lost due to sublimation or melting. The external seeder–feeder process was found to be more important than the internal seeder–feeder process with regard to the impact on precipitation. In the case where the external seeder–feeder process was inhibited, the average surface precipitation and riming rate over the domain were both reduced by 8.5 % and 3.9 %, respectively. When ice–graupel collisions were allowed, further large reductions were seen in the liquid water fraction and riming rate. Inhibiting the internal seeder–feeder process enhanced the liquid water fraction by 6 % compared to a reduction of 5.8 % in the cloud condensate, therefore pointing towards the de-amplification in cloud glaciation and a reduction in surface precipitation. Adding to the observational evidence of frequent seeder–feeder situations, at least over Switzerland, our study highlights the extensive influence of sedimenting ice particles on the properties of feeder clouds as well as on precipitation formation.

For over 6 months, the 2014–2015 effusive eruption at Holuhraun, Iceland, injected considerable amounts of sulfur dioxide (SO2) into the lower troposphere with a daily rate of up to one-third of the global emission rate, causing extensive air pollution across Europe. The large injection of SO2, which oxidises to form sulfate aerosol (SO42-), provides a natural experiment offering an ideal opportunity to scrutinise state-of-the-art general circulation models' (GCMs) representation of aerosol–cloud interactions (ACIs). Here we present Part 1 of a two-part model inter-comparison using the Holuhraun eruption as a framework to analyse ACIs. We use SO2 retrievals from the Infrared Atmospheric Sounding Interferometer (IASI) instrument and ground-based measurements of SO2 and SO42- mass concentrations across Europe, in conjunction with a trajectory analysis using the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model, to assess the spatial and chemical evolution of the volcanic plume as simulated by five GCMs and a chemical transport model (CTM). IASI retrievals of plume altitude and SO2 column load reveal that the volcanic perturbation is largely contained within the lower troposphere. Compared to the satellite observations, the models capture the spatial evolution and vertical variability of the plume reasonably well, although the models often overestimate the plume altitude. HYSPLIT trajectories are used to attribute to Holuhraun emissions 111 instances of elevated sulfurous surface mass concentrations recorded at European Monitoring and Evaluation Programme (EMEP) stations during September and October 2014. Comparisons with the simulated concentrations show that the modelled ratio of SO2 to SO42- during these pollution episodes is often underestimated and overestimated for the young and mature plume, respectively. Models with finer vertical resolutions near the surface are found to better capture these elevated sulfurous ground-level concentrations. Using an exponential function to describe the decay of observed surface mass concentration ratios of SO2 to SO42- with plume age, the in-plume oxidation rate constant is estimated as 0.032 ± 0.002 h−1 (1.30 ± 0.08 d e-folding time), with a near-vent ratio of 25 ± 5 (µg m−3 of SO2 / µg m−3 of SO42-). The majority of the corresponding derived modelled oxidation rate constants are lower than the observed estimate. This suggests that the representation of the oxidation pathway/s in the simulated plumes is too slow. Overall, despite their coarse spatial resolutions, the six models show reasonable skill in capturing the spatial and chemical evolution of the Holuhraun plume. This capable representation of the underlying aerosol perturbation is essential to enable the investigation of the eruption's impact on ACIs in the second part of this study.

To date the climate intervention (CI) proposal of cirrus cloud thinning (CCT) was only assessed in general circulation models (GCMs) using a globally uniform distribution of artificial ice nucleating particles (INPs). In this study, we made the first attempt using the ECHAM–HAM (Hamburg Aerosol Module) GCM to simulate CCT using a fully prognostic cirrus seeding aerosol species. Seeding particles were assumed to be made of bismuth triiodide and were emitted into the atmosphere following aircraft emissions of black carbon (soot). This new approach drastically reduced the number concentration of seeding particles available as INPs in our cirrus ice nucleation sub-model compared to the globally uniform approach. As a result, we found that in order to achieve a significant signal we needed to reduce the assumed radius of emitted seeding particles by an order of magnitude to 0.01 µm and scale the mass emissions of seeding particles by at least a factor of 100 or 1000. This latter scaling factor led to a large net top-of-atmosphere (TOA) warming effect of 5.9 W m−2. This warming effect was a clear response to overseeding with a large concentration of seeding particles (>105 L−1 in the Northern Hemisphere) that was most evident in the tropics. Due to this undesired effect, in a second series of simulations we avoided seeding the tropics by restricting emissions to only the Northern Hemisphere (NH) during winter. We also found a small and insignificant effect, or overseeding, which for the extreme case was reduced compared to the global aircraft emission scenario (2.2 W m−2). Ice crystal radius anomalies were not what we expected, with the largest reduction in size found for the case with a mass scaling factor of 10 instead of the extreme, ×1000, scenario. We attributed this peculiar behavior to the differences in the competition between different seeding particle concentrations and background particles. Finally, we also found that seeding with such large concentrations increased the albedo effect of mixed-phase clouds in the NH due to less efficient cloud droplet consumption, consistent with previous findings from our model. Overall, however, based on this study it is recommended to pause further modeling efforts of CCT unless more observational-based evidence of aerosol–ice-cloud interactions indicates favorable conditions for producing the desired outcome of this CI proposal.

Determining the dominant ice nucleation mode in cirrus is still an open research question that impacts the ability to assess the climate impact of these clouds in numerical models. Homogeneous nucleation is generally well understood. More uncertainty surrounds heterogeneous nucleation due to a weaker understanding of the complex physio-chemical properties (e.g., ice nucleation efficiency and atmospheric abundance) of ice nucleating particles (INPs). This hampers efforts to simulate their interactions with cirrus, which is crucial in order to assess the effect these clouds have on the climate system. Kärcher and Marcolli (2021) introduced a new deterministic heterogeneous ice nucleation parameterization based on the differential activated fraction (AF), which describes the number of INPs that activate ice within a specified temperature or ice saturation ratio interval. They argued that this new approach with explicit INP budgeting, which removes INPs from the total population after they nucleate ice, could help to correct a potential overprediction of heterogeneous nucleation within cirrus when budgeting is not considered. We formulated a general circulation model (GCM)-compatible version of the differential AF parameterization for simulating only deposition nucleation within in situ cirrus and compared it to the method currently employed in the ECHAM6.3-HAM2.3 GCM that is based on cumulative AF. This default cumulative AF approach does not use explicit INP budgeting but instead implicitly budgets for INPs that nucleated ice using a differential ice crystal number concentration variable to calculate whether new ice formation should be added to the pre-existing concentration. In a series of box model simulations that were based on the cirrus sub-model from ECHAM, we found that the cumulative approach likely underpredicts heterogeneous nucleation in cirrus, as it does not account for interstitial INPs remaining from the previous GCM time step. However, as the cases that we simulated in the box model were rather extreme, we extended our analysis to compare the differential and cumulative AF approaches in two simulations in ECHAM-HAM. We find that choosing between these two approaches impacts ice nucleation competition within cirrus in our model. However, based on our 5-year simulations, the small and insignificant difference in the top-of-atmosphere radiative balance of 0.02±0.35 Wm−2 means that the overall climate impact is negligible. We argue that while our GCM-compatible differential AF parameterization is closer to first principles, the default approach based on cumulative AF is simpler due to the lack of additional tracers required. Finally, our new approach could be extended to assess the impact of explicit versus implicit INP budgeting on the ice crystal number concentration produced by immersion freezing of mineral dust particles, as this is also an important mechanism in cirrus.

Cirrus cloud thinning (CCT) is a relatively new radiation management proposal to counteract anthropogenic climate warming by targeting Earth's terrestrial radiation balance. The efficacy of this method was presented in several general circulation model (GCM) studies that showed widely varied radiative responses, originating in part from the differences in the representation of cirrus ice microphysics between the different GCMs. The recent implementation of a new, more physically based ice microphysics scheme (Predicted Particle Properties, P3) that abandons ice hydrometeor size class separation into the ECHAM-HAM GCM, coupled to a new approach for calculating cloud fractions that increases the relative humidity (RH) thresholds for cirrus cloud formation, motivated a reassessment of CCT efficacy. In this study, we first compared CCT sensitivity between the new cloud fraction approach and the original ECHAM-HAM cloud fraction approach. Consistent with previous approaches using ECHAM-HAM, with the P3 scheme and the higher RH thresholds for cirrus cloud formation, we do not find a significant cooling response in any of our simulations. The most notable response from our extreme case is the reduction in the maximum global-mean net top-of-atmosphere (TOA) radiative anomalies from overseeding by about 50 %, from 9.9 W m−2 with the original cloud fraction approach down to 4.9 W m−2 using the new cloud fraction RH thresholds that allow partial grid-box coverage of cirrus clouds above ice saturation, unlike the original approach. Even with this reduction with the updated cloud fraction approach, the TOA anomalies from overseeding far exceed those reported in previous studies. We attribute the large positive TOA anomalies to seeding particles overtaking both homogeneous nucleation and heterogeneous nucleation on mineral dust particles within cirrus clouds to produce more numerous and smaller ice crystals. This effect is amplified by longer ice residence times in clouds due to the slower removal of ice via sedimentation in the P3 scheme. In an effort to avoid this overtaking effect of seeding particles, we increased the default critical ice saturation ratio (Si,seed) for ice nucleation on seeding particles from the default value of 1.05 to 1.35 in a second sensitivity test. With the higher Si,seed we drastically reduce overseeding, which suggests that Si,seed is a key factor to consider for future CCT studies. However, the global-mean TOA anomalies contain high uncertainty. In response, we examined the TOA anomalies regionally and found that specific regions only show a small potential for targeted CCT, which is partially enhanced by using the larger Si,seed. Finally, in a seasonal analysis of TOA responses to CCT, we find that our results do not confirm the previous finding that high-latitude wintertime seeding is a feasible strategy to enhance CCT efficacy, as seeding in our model enhances the already positive cirrus longwave cloud radiative effect for most of our simulations. Our results also show feedbacks on lower-lying mixed-phase and liquid clouds through the reduction in ice crystal sedimentation that reduces cloud droplet depletion and results in stronger cloud albedo effects. However, this is outweighed by stronger longwave trapping from cirrus clouds with more numerous and smaller ice crystals. Therefore, we conclude that CCT is unlikely to act as a feasible climate intervention strategy on a global scale.

Global models are widely used to simulate biomass burning aerosol (BBA). Exhaustive evaluations on model representation of aerosol distributions and properties are fundamental to assess health and climate impacts of BBA. Here we conducted a comprehensive comparison of Aerosol Comparisons between Observations and Models (AeroCom) project model simulations with satellite observations. A total of 59 runs by 18 models from three AeroCom Phase-III experiments (i.e., biomass burning emissions, CTRL16, and CTRL19) and 14 satellite products of aerosols were used in the study. Aerosol optical depth (AOD) at 550 nm was investigated during the fire season over three key fire regions reflecting different fire dynamics (i.e., deforestation-dominated Amazon, Southern Hemisphere Africa where savannas are the key source of emissions, and boreal forest burning in boreal North America). The 14 satellite products were first evaluated against AErosol RObotic NETwork (AERONET) observations, with large uncertainties found. But these uncertainties had small impacts on the model evaluation that was dominated by modeling bias. Through a comparison with Polarization and Directionality of the Earth’s Reflectances measurements with the Generalized Retrieval of Aerosol and Surface Properties algorithm (POLDER-GRASP), we found that the modeled AOD values were biased by −93 % to 152 %, with most models showing significant underestimations even for the state-of-the-art aerosol modeling techniques (i.e., CTRL19). By scaling up BBA emissions, the negative biases in modeled AOD were significantly mitigated, although it yielded only negligible improvements in the correlation between models and observations, and the spatial and temporal variations in AOD biases did not change much. For models in CTRL16 and CTRL19, the large diversity in modeled AOD was in almost equal measures caused by diversity in emissions, lifetime, and the mass extinction coefficient (MEC). We found that in the AeroCom ensemble, BBA lifetime correlated significantly with particle deposition (as expected) and in turn correlated strongly with precipitation. Additional analysis based on Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP) aerosol profiles suggested that the altitude of the aerosol layer in the current models was generally too low, which also contributed to the bias in modeled lifetime. Modeled MECs exhibited significant correlations with the Ångström exponent (AE, an indicator of particle size). Comparisons with the POLDER-GRASP-observed AE suggested that the models tended to overestimate the AE (underestimated particle size), indicating a possible underestimation of MECs in models. The hygroscopic growth in most models generally agreed with observations and might not explain the overall underestimation of modeled AOD. Our results imply that current global models contain biases in important aerosol processes for BBA (e.g., emissions, removal, and optical properties) that remain to be addressed in future research.

Cloud properties and their evolution influence Earth's radiative balance. The cloud microphysical (CMP) processes that shape these properties are therefore important to represent in global climate models. Historically, parameterizations in these models have grown more detailed and complex. However, a simpler formulation of CMP processes may leave the model results mostly unchanged while enabling an easier interpretation of model results and helping to increase process understanding. This study employs sensitivity analysis of an emulated perturbed parameter ensemble of the global aerosol–climate model ECHAM-HAM to illuminate the impact of selected CMP cloud ice processes on model output. The response to the perturbation of a process serves as a proxy for the effect of a simplification. Autoconversion of ice crystals is found to be the dominant CMP process in influencing key variables such as the ice water path and cloud radiative effects, while riming of cloud droplets on snow has the most influence on the liquid phase. Accretion of ice and snow and self-collection of ice crystals have a negligible influence on model output and are therefore identified as suitable candidates for future simplifications. In turn, the dominating role of autoconversion suggests that this process has the greatest need to be represented correctly. A seasonal and spatially resolved analysis employing a spherical harmonics expansion of the data corroborates the results. This study introduces a new application for the combination of statistical emulation and sensitivity analysis to evaluate the sensitivity of a complex numerical model to a specific parameterized process. It paves the way for simplifications of CMP processes leading to more interpretable climate model results.

In this study, we implement a vertical grid refinement scheme in the radiation routine of the global aerosol–climate model ECHAM-HAM, aiming to improve the representation of stratocumulus clouds and address the underestimation of their cloud cover. The scheme is based on a reconstruction of the temperature inversion as a physical constraint for the cloud top. On the refined grid, the boundary layer and the free troposphere are separated and the cloud's layer is made thinner. The cloud cover is recalculated either by conserving the cloud volume (SC-VOLUME) or by using the Sundqvist cloud cover routine on the new grid representation (SC-SUND). In global climate simulations, we find that the SC-VOLUME approach is inadequate, as there is a mismatch, in most cases, between the layer of the inversion and the layer of the stratocumulus cloud, which prevents its application and is itself likely caused by an overly low vertical resolution. Additionally, we find that the occurrence frequency of stratocumulus clouds is underestimated in ECHAM-HAM, limiting a priori the potential benefits of a scheme like SC-VOLUME targeting only cloud amount when present. With the SC-SUND approach, the possibility for new clouds to be formed on the refined grid results in a large increase in mean total cloud cover in stratocumulus regions. In both cases, however, the changes exerted in the radiation routine are too weak to produce a significant improvement in the simulated stratocumulus cloud cover. We investigate and discuss the reasons behind this. The grid refinement scheme could be used more effectively for this purpose if implemented directly in the model's cloud microphysics and cloud cover routines, but other possible ways forward are also discussed.

Clouds and cloud feedbacks represent one of the largest uncertainties in climate projections. As the ice phase influences many key cloud properties and their lifetime, its formation needs to be better understood in order to improve climate and weather prediction models. Ice crystals sedimenting out of a cloud do not sublimate immediately but can survive certain distances and eventually fall into a cloud below. This natural cloud seeding can trigger glaciation and has been shown to enhance precipitation formation. However, to date, an estimate of its occurrence frequency is lacking. In this study, we estimate the occurrence frequency of natural cloud seeding over Switzerland from satellite data and sublimation calculations. We use the DARDAR (radar lidar) satellite product between April 2006 and October 2017 to estimate the occurrence frequency of multi-layer cloud situations, where a cirrus cloud at T < −35 ∘C can provide seeds to a lower-lying feeder cloud. These situations are found to occur in 31 % of the observations. Of these, 42 % have a cirrus cloud above another cloud, separated, while in 58 % the cirrus is part of a thicker cloud, with a potential for in-cloud seeding. Vertical distances between the cirrus and the lower-lying cloud are distributed uniformly between 100 m and 10 km. They are found to not vary with topography. Seasonally, winter nights have the most multi-layer cloud occurrences, in 38 % of the measurements. Additionally, in situ and liquid origin cirrus cloud size modes can be identified according to the ice crystal mean effective radius in the DARDAR data. Using sublimation calculations, we show that in a significant number of cases the seeding ice crystals do not sublimate before reaching the lower-lying feeder cloud. Depending on whether bullet rosette, plate-like or spherical crystals were assumed, 10 %, 11 % or 20 % of the crystals, respectively, could provide seeds after sedimenting 2 km. The high occurrence frequency of seeding situations and the survival of the ice crystals indicate that the seeder–feeder process and natural cloud seeding are widespread phenomena over Switzerland. This hints at a large potential for natural cloud seeding to influence cloud properties and thereby the Earth's radiative budget and water cycle, which should be studied globally. Further investigations of the magnitude of the seeding ice crystals' effect on lower-lying clouds are necessary to estimate the contribution of natural cloud seeding to precipitation.

Abstract The Hamburg Aerosol Module version 2.3 (HAM2.3) from the ECHAM6.3‐HAM2.3 global atmosphere‐aerosol model is coupled to the recently developed icosahedral nonhydrostatic ICON‐A (icon‐aes‐1.3.00) global atmosphere model to yield the new ICON‐A‐HAM2.3 atmosphere‐aerosol model. The ICON‐A and ECHAM6.3 host models use different dynamical cores, parameterizations of vertical mixing due to sub‐grid scale turbulence, and parameter settings for radiation balance tuning. Here, we study the role of the different host models for simulated aerosol optical thickness (AOT) and evaluate impacts of using HAM2.3 and the ECHAM6‐HAM2.3 two‐moment cloud microphysics scheme on several meteorological variables. Sensitivity runs show that a positive AOT bias over the subtropical oceans is remedied in ICON‐A‐HAM2.3 because of a different default setting of a parameter in the moist convection parameterization of the host models. The global mean AOT is biased low compared to MODIS satellite instrument retrievals in ICON‐A‐HAM2.3 and ECHAM6.3‐HAM2.3, but the bias is larger in ICON‐A‐HAM2.3 because negative AOT biases over the Amazon, the African rain forest, and the northern Indian Ocean are no longer compensated by high biases over the sub‐tropical oceans. ICON‐A‐HAM2.3 shows a moderate improvement with respect to AOT observations at AERONET sites. A multivariable bias score combining biases of several meteorological variables into a single number is larger in ICON‐A‐HAM2.3 compared to standard ICON‐A and standard ECHAM6.3. In the tropics, this multivariable bias is of similar magnitude in ICON‐A‐HAM2.3 and in ECHAM6.3‐HAM2.3. In the extra‐tropics, a smaller multivariable bias is found for ICON‐A‐HAM2.3 than for ECHAM6.3‐HAM2.3.

Within the framework of the AeroCom (Aerosol Comparisons between Observations and Models) initiative, the state-of-the-art modelling of aerosol optical properties is assessed from 14 global models participating in the phase III control experiment (AP3). The models are similar to CMIP6/AerChemMIP Earth System Models (ESMs) and provide a robust multi-model ensemble. Inter-model spread of aerosol species lifetimes and emissions appears to be similar to that of mass extinction coefficients (MECs), suggesting that aerosol optical depth (AOD) uncertainties are associated with a broad spectrum of parameterised aerosol processes. Total AOD is approximately the same as in AeroCom phase I (AP1) simulations. However, we find a 50 % decrease in the optical depth (OD) of black carbon (BC), attributable to a combination of decreased emissions and lifetimes. Relative contributions from sea salt (SS) and dust (DU) have shifted from being approximately equal in AP1 to SS contributing about 2∕3 of the natural AOD in AP3. This shift is linked with a decrease in DU mass burden, a lower DU MEC, and a slight decrease in DU lifetime, suggesting coarser DU particle sizes in AP3 compared to AP1. Relative to observations, the AP3 ensemble median and most of the participating models underestimate all aerosol optical properties investigated, that is, total AOD as well as fine and coarse AOD (AODf, AODc), Ångström exponent (AE), dry surface scattering (SCdry), and absorption (ACdry) coefficients. Compared to AERONET, the models underestimate total AOD by ca. 21 % ± 20 % (as inferred from the ensemble median and interquartile range). Against satellite data, the ensemble AOD biases range from −37 % (MODIS-Terra) to −16 % (MERGED-FMI, a multi-satellite AOD product), which we explain by differences between individual satellites and AERONET measurements themselves. Correlation coefficients (R) between model and observation AOD records are generally high (R>0.75), suggesting that the models are capable of capturing spatio-temporal variations in AOD. We find a much larger underestimate in coarse AODc (∼ −45 % ± 25 %) than in fine AODf (∼ −15 % ± 25 %) with slightly increased inter-model spread compared to total AOD. These results indicate problems in the modelling of DU and SS. The AODc bias is likely due to missing DU over continental land masses (particularly over the United States, SE Asia, and S. America), while marine AERONET sites and the AATSR SU satellite data suggest more moderate oceanic biases in AODc. Column AEs are underestimated by about 10 % ± 16 %. For situations in which measurements show AE > 2, models underestimate AERONET AE by ca. 35 %. In contrast, all models (but one) exhibit large overestimates in AE when coarse aerosol dominates (bias ca. +140 % if observed AE < 0.5). Simulated AE does not span the observed AE variability. These results indicate that models overestimate particle size (or underestimate the fine-mode fraction) for fine-dominated aerosol and underestimate size (or overestimate the fine-mode fraction) for coarse-dominated aerosol. This must have implications for lifetime, water uptake, scattering enhancement, and the aerosol radiative effect, which we can not quantify at this moment. Comparison against Global Atmosphere Watch (GAW) in situ data results in mean bias and inter-model variations of −35 % ± 25 % and −20 % ± 18 % for SCdry and ACdry, respectively. The larger underestimate of SCdry than ACdry suggests the models will simulate an aerosol single scattering albedo that is too low. The larger underestimate of SCdry than ambient air AOD is consistent with recent findings that models overestimate scattering enhancement due to hygroscopic growth. The broadly consistent negative bias in AOD and surface scattering suggests an underestimate of aerosol radiative effects in current global aerosol models. Considerable inter-model diversity in the simulated optical properties is often found in regions that are, unfortunately, not or only sparsely covered by ground-based observations. This includes, for instance, the Sahara, Amazonia, central Australia, and the South Pacific. This highlights the need for a better site coverage in the observations, which would enable us to better assess the models, but also the performance of satellite products in these regions. Using fine-mode AOD as a proxy for present-day aerosol forcing estimates, our results suggest that models underestimate aerosol forcing by ca. −15 %, however, with a considerably large interquartile range, suggesting a spread between −35 % and +10 %.

Poor air quality is currently responsible for large impacts on human health across the world. In addition, the air pollutants ozone (O3) and particulate matter less than 2.5 µm in diameter (PM2.5) are also radiatively active in the atmosphere and can influence Earth's climate. It is important to understand the effect of air quality and climate mitigation measures over the historical period and in different future scenarios to ascertain any impacts from air pollutants on both climate and human health. The Coupled Model Intercomparison Project Phase 6 (CMIP6) presents an opportunity to analyse the change in air pollutants simulated by the current generation of climate and Earth system models that include a representation of chemistry and aerosols (particulate matter). The shared socio-economic pathways (SSPs) used within CMIP6 encompass a wide range of trajectories in precursor emissions and climate change, allowing for an improved analysis of future changes to air pollutants. Firstly, we conduct an evaluation of the available CMIP6 models against surface observations of O3 and PM2.5. CMIP6 models consistently overestimate observed surface O3 concentrations across most regions and in most seasons by up to 16 ppb, with a large diversity in simulated values over Northern Hemisphere continental regions. Conversely, observed surface PM2.5 concentrations are consistently underestimated in CMIP6 models by up to 10 µg m−3, particularly for the Northern Hemisphere winter months, with the largest model diversity near natural emission source regions. The biases in CMIP6 models when compared to observations of O3 and PM2.5 are similar to those found in previous studies. Over the historical period (1850–2014) large increases in both surface O3 and PM2.5 are simulated by the CMIP6 models across all regions, particularly over the mid to late 20th century, when anthropogenic emissions increase markedly. Large regional historical changes are simulated for both pollutants across East and South Asia with an annual mean increase of up to 40 ppb for O3 and 12 µg m−3 for PM2.5. In future scenarios containing strong air quality and climate mitigation measures (ssp126), annual mean concentrations of air pollutants are substantially reduced across all regions by up to 15 ppb for O3 and 12 µg m−3 for PM2.5. However, for scenarios that encompass weak action on mitigating climate and reducing air pollutant emissions (ssp370), annual mean increases in both surface O3 (up 10 ppb) and PM2.5 (up to 8 µg m−3) are simulated across most regions, although, for regions like North America and Europe small reductions in PM2.5 are simulated due to the regional reduction in precursor emissions in this scenario. A comparison of simulated regional changes in both surface O3 and PM2.5 from individual CMIP6 models highlights important regional differences due to the simulated interaction of aerosols, chemistry, climate and natural emission sources within models. The projection of regional air pollutant concentrations from the latest climate and Earth system models used within CMIP6 shows that the particular future trajectory of climate and air quality mitigation measures could have important consequences for regional air quality, human health and near-term climate. Differences between individual models emphasise the importance of understanding how future Earth system feedbacks influence natural emission sources, e.g. response of biogenic emissions under climate change.

This study presents a multiparameter analysis of aerosol trends over the last 2 decades at regional and global scales. Regional time series have been computed for a set of nine optical, chemical-composition and mass aerosol properties by using the observations from several ground-based networks. From these regional time series the aerosol trends have been derived for the different regions of the world. Most of the properties related to aerosol loading exhibit negative trends, both at the surface and in the total atmospheric column. Significant decreases in aerosol optical depth (AOD) are found in Europe, North America, South America, North Africa and Asia, ranging from −1.2 % yr−1 to −3.1 % yr−1. An error and representativity analysis of the spatially and temporally limited observational data has been performed using model data subsets in order to investigate how much the observed trends represent the actual trends happening in the regions over the full study period from 2000 to 2014. This analysis reveals that significant uncertainty is associated with some of the regional trends due to time and space sampling deficiencies. The set of observed regional trends has then been used for the evaluation of 10 models (6 AeroCom phase III models and 4 CMIP6 models) and the CAMS reanalysis dataset and of their skills in reproducing the aerosol trends. Model performance is found to vary depending on the parameters and the regions of the world. The models tend to capture trends in AOD, the column Ångström exponent, sulfate and particulate matter well (except in North Africa), but they show larger discrepancies for coarse-mode AOD. The rather good agreement of the trends, across different aerosol parameters between models and observations, when co-locating them in time and space, implies that global model trends, including those in poorly monitored regions, are likely correct. The models can help to provide a global picture of the aerosol trends by filling the gaps in regions not covered by observations. The calculation of aerosol trends at a global scale reveals a different picture from that depicted by solely relying on ground-based observations. Using a model with complete diagnostics (NorESM2), we find a global increase in AOD of about 0.2 % yr−1 between 2000 and 2014, primarily caused by an increase in the loads of organic aerosols, sulfate and black carbon.

It is important to understand how future environmental policies will impact both climate change and air pollution. Although targeting near-term climate forcers (NTCFs), defined here as aerosols, tropospheric ozone, and precursor gases, should improve air quality, NTCF reductions will also impact climate. Prior assessments of the impact of NTCF mitigation on air quality and climate have been limited. This is related to the idealized nature of some prior studies, simplified treatment of aerosols and chemically reactive gases, as well as a lack of a sufficiently large number of models to quantify model diversity and robust responses. Here, we quantify the 2015–2055 climate and air quality effects of non-methane NTCFs using nine state-of-the-art chemistry–climate model simulations conducted for the Aerosol and Chemistry Model Intercomparison Project (AerChemMIP). Simulations are driven by two future scenarios featuring similar increases in greenhouse gases (GHGs) but with “weak” (SSP3-7.0) versus “strong” (SSP3-7.0-lowNTCF) levels of air quality control measures. As SSP3-7.0 lacks climate policy and has the highest levels of NTCFs, our results (e.g., surface warming) represent an upper bound. Unsurprisingly, we find significant improvements in air quality under NTCF mitigation (strong versus weak air quality controls). Surface fine particulate matter (PM2.5) and ozone (O3) decrease by -2.2±0.32 µg m−3 and -4.6±0.88 ppb, respectively (changes quoted here are for the entire 2015–2055 time period; uncertainty represents the 95 % confidence interval), over global land surfaces, with larger reductions in some regions including south and southeast Asia. Non-methane NTCF mitigation, however, leads to additional climate change due to the removal of aerosol which causes a net warming effect, including global mean surface temperature and precipitation increases of 0.25±0.12 K and 0.03±0.012 mm d−1, respectively. Similarly, increases in extreme weather indices, including the hottest and wettest days, also occur. Regionally, the largest warming and wetting occurs over Asia, including central and north Asia (0.66±0.20 K and 0.03±0.02 mm d−1), south Asia (0.47±0.16 K and 0.17±0.09 mm d−1), and east Asia (0.46±0.20 K and 0.15±0.06 mm d−1). Relatively large warming and wetting of the Arctic also occur at 0.59±0.36 K and 0.04±0.02 mm d−1, respectively. Similar surface warming occurs in model simulations with aerosol-only mitigation, implying weak cooling due to ozone reductions. Our findings suggest that future policies that aggressively target non-methane NTCF reductions will improve air quality but will lead to additional surface warming, particularly in Asia and the Arctic. Policies that address other NTCFs including methane, as well as carbon dioxide emissions, must also be adopted to meet climate mitigation goals.

The evaluation of modelling diagnostics with appropriate observations is an important task that establishes the capabilities and reliability of models. In this study we compare aerosol and cloud properties obtained from three different climate models (ECHAM-HAM, ECHAM-HAM-SALSA, and NorESM) with satellite observations using Moderate Resolution Imaging Spectroradiometer (MODIS) data. The simulator MODIS-COSP version 1.4 was implemented into the climate models to obtain MODIS-like cloud diagnostics, thus enabling model-to-model and model-to-satellite comparisons. Cloud droplet number concentrations (CDNCs) are derived identically from MODIS-COSP-simulated and MODIS-retrieved values of cloud optical depth and effective radius. For CDNC, the models capture the observed spatial distribution of higher values typically found near the coasts, downwind of the major continents, and lower values over the remote ocean and land areas. However, the COSP-simulated CDNC values are higher than those observed, whilst the direct model CDNC output is significantly lower than the MODIS-COSP diagnostics. NorESM produces large spatial biases for ice cloud properties and thick clouds over land. Despite having identical cloud modules, ECHAM-HAM and ECHAM-HAM-SALSA diverge in their representation of spatial and vertical distributions of clouds. From the spatial distributions of aerosol optical depth (AOD) and aerosol index (AI), we find that NorESM shows large biases for AOD over bright land surfaces, while discrepancies between ECHAM-HAM and ECHAM-HAM-SALSA can be observed mainly over oceans. Overall, the AIs from the different models are in good agreement globally, with higher negative biases in the Northern Hemisphere. We evaluate the aerosol–cloud interactions by computing the sensitivity parameter ACICDNC=dln⁡(CDNC)/dln⁡(AI) on a global scale. However, 1 year of data may be considered not enough to assess the similarity or dissimilarities of the models due to large temporal variability in cloud properties. This study shows how simulators facilitate the evaluation of cloud properties and expose model deficiencies, which are necessary steps to further improve the parameterisation in climate models.

The radiative forcing from aerosols (particularly through their interaction with clouds) remains one of the most uncertain components of the human forcing of the climate. Observation-based studies have typically found a smaller aerosol effective radiative forcing than in model simulations and were given preferential weighting in the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5). With their own sources of uncertainty, it is not clear that observation-based estimates are more reliable. Understanding the source of the model and observational differences is thus vital to reduce uncertainty in the impact of aerosols on the climate. These reported discrepancies arise from the different methods of separating the components of aerosol forcing used in model and observational studies. Applying the observational decomposition to global climate model (GCM) output, the two different lines of evidence are surprisingly similar, with a much better agreement on the magnitude of aerosol impacts on cloud properties. Cloud adjustments remain a significant source of uncertainty, particularly for ice clouds. However, they are consistent with the uncertainty from observation-based methods, with the liquid water path adjustment usually enhancing the Twomey effect by less than 50 %. Depending on different sets of assumptions, this work suggests that model and observation-based estimates could be more equally weighted in future synthesis studies.

The largest contributors to the uncertainty in assessing the anthropogenic contribution in radiative forcing are the direct and indirect effects of aerosol particles on the Earth's radiative budget. Soot particles are of special interest since their properties can change significantly due to aging processes once they are emitted into the atmosphere. Probably the largest obstacle for the investigation of these processes in the laboratory is the long atmospheric lifetime of 1 week, requiring tailored experiments that cover this time span. This work presents results on the ability of two types of soot, obtained using a miniCAST soot generator, to act as cloud condensation nuclei (CCN) after exposure to atmospherically relevant levels of ozone (O3) and humidity. Aging times of up to 12 h were achieved by successful application of the continuous-flow stirred tank reactor (CSTR) concept while allowing for size selection of particles prior to the aging step. Particles of 100 nm diameter and rich in organic carbon (OC) that were initially CCN inactive showed significant CCN activity at supersaturations (SS) down to 0.3 % after 10 h of exposure to 200 ppb of O3. While this process was not affected by different levels of relative humidity in the range of 5 %–75 %, a high sensitivity towards the ambient/reaction temperature was observed. Soot particles with a lower OC content required an approximately 4-fold longer aging duration to show CCN activity at the same SS. Prior to the slow change in the CCN activity, a rapid increase in the particle diameter was detected which occurred within several minutes. This study highlights the applicability of the CSTR approach for the simulation of atmospheric aging processes, as aging durations beyond 12 h can be achieved in comparably small aerosol chamber volumes ( m3). Implementation of our measurement results in a global aerosol-climate model, ECHAM6.3-HAM2.3, showed a statistically significant increase in the regional and global CCN burden and cloud droplet number concentration.

The global aerosol–climate model ECHAM6.3–HAM2.3 (E63H23) as well as the previous model versions ECHAM5.5–HAM2.0 (E55H20) and ECHAM6.1–HAM2.2 (E61H22) are evaluated using global observational datasets for clouds and precipitation. In E63H23, the amount of low clouds, the liquid and ice water path, and cloud radiative effects are more realistic than in previous model versions. E63H23 has a more physically based aerosol activation scheme, improvements in the cloud cover scheme, changes in the detrainment of convective clouds, changes in the sticking efficiency for the accretion of ice crystals by snow, consistent ice crystal shapes throughout the model, and changes in mixed-phase freezing; an inconsistency in ice crystal number concentration (ICNC) in cirrus clouds was also removed. Common biases in ECHAM and in E63H23 (and in previous ECHAM–HAM versions) are a cloud amount in stratocumulus regions that is too low and deep convective clouds over the Atlantic and Pacific oceans that form too close to the continents (while tropical land precipitation is underestimated). There are indications that ICNCs are overestimated in E63H23. Since clouds are important for effective radiative forcing due to aerosol–radiation and aerosol–cloud interactions (ERFari+aci) and equilibrium climate sensitivity (ECS), differences in ERFari+aci and ECS between the model versions were also analyzed. ERFari+aci is weaker in E63H23 (−1.0 W m−2) than in E61H22 (−1.2 W m−2) (or E55H20; −1.1 W m−2). This is caused by the weaker shortwave ERFari+aci (a new aerosol activation scheme and sea salt emission parameterization in E63H23, more realistic simulation of cloud water) overcompensating for the weaker longwave ERFari+aci (removal of an inconsistency in ICNC in cirrus clouds in E61H22). The decrease in ECS in E63H23 (2.5 K) compared to E61H22 (2.8 K) is due to changes in the entrainment rate for shallow convection (affecting the cloud amount feedback) and a stronger cloud phase feedback. Experiments with minimum cloud droplet number concentrations (CDNCmin) of 40 cm−3 or 10 cm−3 show that a higher value of CDNCmin reduces ERFari+aci as well as ECS in E63H23.

A total of 16 global chemistry transport models and general circulation models have participated in this study; 14 models have been evaluated with regard to their ability to reproduce the near-surface observed number concentration of aerosol particles and cloud condensation nuclei (CCN), as well as derived cloud droplet number concentration (CDNC). Model results for the period 2011–2015 are compared with aerosol measurements (aerosol particle number, CCN and aerosol particle composition in the submicron fraction) from nine surface stations located in Europe and Japan. The evaluation focuses on the ability of models to simulate the average across time state in diverse environments and on the seasonal and short-term variability in the aerosol properties. There is no single model that systematically performs best across all environments represented by the observations. Models tend to underestimate the observed aerosol particle and CCN number concentrations, with average normalized mean bias (NMB) of all models and for all stations, where data are available, of −24 % and −35 % for particles with dry diameters >50 and >120 nm, as well as −36 % and −34 % for CCN at supersaturations of 0.2 % and 1.0 %, respectively. However, they seem to behave differently for particles activating at very low supersaturations ( %) than at higher ones. A total of 15 models have been used to produce ensemble annual median distributions of relevant parameters. The model diversity (defined as the ratio of standard deviation to mean) is up to about 3 for simulated N3 (number concentration of particles with dry diameters larger than 3 nm) and up to about 1 for simulated CCN in the extra-polar regions. A global mean reduction of a factor of about 2 is found in the model diversity for CCN at a supersaturation of 0.2 % (CCN0.2) compared to that for N3, maximizing over regions where new particle formation is important. An additional model has been used to investigate potential causes of model diversity in CCN and bias compared to the observations by performing a perturbed parameter ensemble (PPE) accounting for uncertainties in 26 aerosol-related model input parameters. This PPE suggests that biogenic secondary organic aerosol formation and the hygroscopic properties of the organic material are likely to be the major sources of CCN uncertainty in summer, with dry deposition and cloud processing being dominant in winter. Models capture the relative amplitude of the seasonal variability of the aerosol particle number concentration for all studied particle sizes with available observations (dry diameters larger than 50, 80 and 120 nm). The short-term persistence time (on the order of a few days) of CCN concentrations, which is a measure of aerosol dynamic behavior in the models, is underestimated on average by the models by 40 % during winter and 20 % in summer. In contrast to the large spread in simulated aerosol particle and CCN number concentrations, the CDNC derived from simulated CCN spectra is less diverse and in better agreement with CDNC estimates consistently derived from the observations (average NMB −13 % and −22 % for updraft velocities 0.3 and 0.6 m s−1, respectively). In addition, simulated CDNC is in slightly better agreement with observationally derived values at lower than at higher updraft velocities (index of agreement 0.64 vs. 0.65). The reduced spread of CDNC compared to that of CCN is attributed to the sublinear response of CDNC to aerosol particle number variations and the negative correlation between the sensitivities of CDNC to aerosol particle number concentration (∂Nd/∂Na) and to updraft velocity (∂Nd/∂w). Overall, we find that while CCN is controlled by both aerosol particle number and composition, CDNC is sensitive to CCN at low and moderate CCN concentrations and to the updraft velocity when CCN levels are high. Discrepancies are found in sensitivities ∂Nd/∂Na and ∂Nd/∂w; models may be predisposed to be too “aerosol sensitive” or “aerosol insensitive” in aerosol–cloud–climate interaction studies, even if they may capture average droplet numbers well. This is a subtle but profound finding that only the sensitivities can clearly reveal and may explain inter-model biases on the aerosol indirect effect.

Cloud microphysics schemes in global climate models have long suffered from a lack of reliable satellite observations of cloud ice. At the same time there is a broad consensus that the correct simulation of cloud phase is imperative for a reliable assessment of Earth's climate sensitivity. At the core of this problem is understanding the causes for the inter-model spread of the predicted cloud phase partitioning. This work introduces a new method to build a sound cause-and-effect relation between the microphysical parameterizations employed in our model and the resulting cloud field by analysing ice formation pathways. We find that freezing processes in supercooled liquid clouds only dominate ice formation in roughly 6 % of the simulated clouds, a small fraction compared to roughly 63 % of the clouds governed by freezing in the cirrus temperature regime below −35 ∘C. This pathway analysis further reveals that even in the mixed-phase temperature regime between −35 and 0 ∘C, the dominant source of ice is the sedimentation of ice crystals that originated in the cirrus regime. The simulated fraction of ice cloud to total cloud amount in our model is lower than that reported by the CALIPSO-GOCCP satellite product. This is most likely caused by structural differences of the cloud and aerosol fields in our model rather than the microphysical parametrizations employed.

We introduce and evaluate aerosol simulations with the global aerosol–climate model ECHAM6.3–HAM2.3, which is the aerosol component of the fully coupled aerosol–chemistry–climate model ECHAM–HAMMOZ. Both the host atmospheric climate model ECHAM6.3 and the aerosol model HAM2.3 were updated from previous versions. The updated version of the HAM aerosol model contains improved parameterizations of aerosol processes such as cloud activation, as well as updated emission fields for anthropogenic aerosol species and modifications in the online computation of sea salt and mineral dust aerosol emissions. Aerosol results from nudged and free-running simulations for the 10-year period 2003 to 2012 are compared to various measurements of aerosol properties. While there are regional deviations between the model and observations, the model performs well overall in terms of aerosol optical thickness, but may underestimate coarse-mode aerosol concentrations to some extent so that the modeled particles are smaller than indicated by the observations. Sulfate aerosol measurements in the US and Europe are reproduced well by the model, while carbonaceous aerosol species are biased low. Both mineral dust and sea salt aerosol concentrations are improved compared to previous versions of ECHAM–HAM. The evaluation of the simulated aerosol distributions serves as a basis for the suitability of the model for simulating aerosol–climate interactions in a changing climate.

Ice crystal submicron structures have a large impact on the optical properties of cirrus clouds and consequently on their radiative effect. Although there is growing evidence that atmospheric ice crystals are rarely pristine, direct in situ observations of the degree of ice crystal complexity are largely missing. Here we show a comprehensive in situ data set of ice crystal complexity coupled with measurements of the cloud angular scattering functions collected during a number of observational airborne campaigns at diverse geographical locations. Our results demonstrate that an overwhelming fraction (between 61 % and 81 %) of atmospheric ice crystals sampled in the different regions contain mesoscopic deformations and, as a consequence, a similar flat and featureless angular scattering function is observed. A comparison between the measurements and a database of optical particle properties showed that severely roughened hexagonal aggregates optimally represent the measurements in the observed angular range. Based on this optical model, a new parameterization of the cloud bulk asymmetry factor was introduced and its effects were tested in a global climate model. The modelling results suggest that, due to ice crystal complexity, ice-containing clouds can induce an additional short-wave cooling effect of −1.12 W m2 on the top-of-the-atmosphere radiative budget that has not yet been considered.

In this paper, we present the implementation and evaluation of the aerosol microphysics module SALSA2.0 in the framework of the aerosol–chemistry–climate model ECHAM-HAMMOZ. It is an alternative microphysics module to the default modal microphysics scheme M7 in ECHAM-HAMMOZ. The SALSA2.0 implementation within ECHAM-HAMMOZ is evaluated against observations of aerosol optical properties, aerosol mass, and size distributions, comparing also to the skill of the M7 implementation. The largest differences between the implementation of SALSA2.0 and M7 are in the methods used for calculating microphysical processes, i.e., nucleation, condensation, coagulation, and hydration. These differences in the microphysics are reflected in the results so that the largest differences between SALSA2.0 and M7 are evident over regions where the aerosol size distribution is heavily modified by the microphysical processing of aerosol particles. Such regions are, for example, highly polluted regions and regions strongly affected by biomass burning. In addition, in a simulation of the 1991 Mt. Pinatubo eruption in which a stratospheric sulfate plume was formed, the global burden and the effective radii of the stratospheric aerosol are very different in SALSA2.0 and M7. While SALSA2.0 was able to reproduce the observed time evolution of the global burden of sulfate and the effective radii of stratospheric aerosol, M7 strongly overestimates the removal of coarse stratospheric particles and thus underestimates the effective radius of stratospheric aerosol. As the mode widths of M7 have been optimized for the troposphere and were not designed to represent stratospheric aerosol, the ability of M7 to simulate the volcano plume was improved by modifying the mode widths, decreasing the standard deviations of the accumulation and coarse modes from 1.59 and 2.0, respectively, to 1.2 similar to what was observed after the Mt. Pinatubo eruption. Overall, SALSA2.0 shows promise in improving the aerosol description of ECHAM-HAMMOZ and can be further improved by implementing methods for aerosol processes that are more suitable for the sectional method, e.g., size-dependent emissions for aerosol species and size-resolved wet deposition.

Future sea ice retreat in the Arctic in summer and autumn is expected to affect both natural and anthropogenic aerosol emissions: sea ice acts as a barrier between the ocean and the atmosphere, and reducing it increases dimethyl sulfide and sea salt emissions. Additionally, a decrease in the area and thickness of sea ice could lead to enhanced Arctic ship traffic, for example due to shorter routes of cargo ships. Changes in the emissions of aerosol particles can then influence cloud properties, precipitation, surface albedo, and radiation. Next to changes in aerosol emissions, clouds will also be affected by increases in Arctic temperatures and humidities. In this study, we quantify how future aerosol radiative forcings and cloud radiative effects might change in the Arctic in late summer (July–August) and early autumn (September–October).Simulations were conducted for the years 2004 and 2050 with the global aerosol–climate model ECHAM6-HAM2. For 2050, simulations with and without additional ship emissions in the Arctic were carried out to quantify the impact of these emissions on the Arctic climate.In the future, sea salt as well as dimethyl sulfide emissions and burdens will increase in the Arctic. The increase in cloud condensation nuclei, which is due to changes in aerosol particles and meteorology, will enhance cloud droplet number concentrations over the Arctic Ocean (+10 % in late summer and +29 % in early autumn; in-cloud values averaged between 75 and 90° N). Furthermore, both liquid and total water path will increase (+10 % and +8 % in late summer; +34 % and +26 % in early autumn) since the specific humidity will be enhanced due to higher temperatures and the exposure of the ocean's surface.Changes in both aerosol radiative forcings and cloud radiative effects at the top of the atmosphere will not be dominated by the aerosol particles and clouds themselves but by the decrease in surface albedo (and by the increase in surface temperature for the longwave cloud radiative effect in early autumn). Mainly due to the reduction in sea ice, the aerosol radiative forcing will become less positive (decreasing from 0.53 to 0.36 W m−2 in late summer and from 0.15 to 0.11 W m−2 in early autumn). The decrease in sea ice is also mainly responsible for changes in the net cloud radiative effect, which will become more negative in late summer (changing from −36 to −46 W m−2). Therefore, the cooling component of both aerosols and clouds will gain importance in the future.We found that future Arctic ship emissions related to transport and oil and gas extraction (Peters et al. 2011) will not have a large impact on clouds and radiation: changes in aerosols only become significant when we increase these ship emissions by a factor of 10. However, even with 10-fold ship emissions, the net aerosol radiative forcing shows no significant changes. Enhanced black carbon deposition on snow leads to a locally significant but very small increase in radiative forcing over the central Arctic Ocean in early autumn (no significant increase for average between 75 and 90° N). Furthermore, the 10-fold higher ship emissions increase the optical thickness and lifetime of clouds in late summer (net cloud radiative effect changing from −48 to −52 W m−2). These aerosol–cloud effects have a considerably larger influence on the radiative forcing than the direct effects of particles (both aerosol particles in the atmosphere and particles deposited on snow). In summary, future ship emissions of aerosols and their precursor gases might have a net cooling effect, which is small compared to other changes in future Arctic climate such as those caused by the decrease in surface albedo.

How clouds change in a warmer climate remains one of the largest uncertainties for the equilibrium climate sensitivity (ECS). While a large spread in the cloud feedback arises from low-level clouds, it was recently shown that mixed-phase clouds are also important for ECS. If mixed-phase clouds in the current climate contain too few supercooled cloud droplets, too much ice will change to liquid water in a warmer climate. As shown by Tan et al. (2016), this overestimates the negative cloud-phase feedback and underestimates ECS in the CAM global climate model (GCM). Here we use the newest version of the ECHAM6-HAM2 GCM to investigate the importance of mixed-phase and ice clouds for ECS. Although we also considerably underestimate the fraction of supercooled liquid water globally in the reference version of the ECHAM6-HAM2 GCM, we do not obtain increases in ECS in simulations with more supercooled liquid water in the present-day climate, different from the findings by Tan et al. (2016). We hypothesize that it is not the global supercooled liquid water fraction that matters, but only how well low- and mid-level mixed-phase clouds with cloud-top temperatures in the mixed-phase temperature range between 0 and −35 °C that are not shielded by higher-lying ice clouds are simulated. These occur most frequently in midlatitudes, in particular over the Southern Ocean where they determine the amount of absorbed shortwave radiation. In ECHAM6-HAM2 the amount of absorbed shortwave radiation over the Southern Ocean is only significantly overestimated if all clouds below 0 °C consist exclusively of ice. Only in this simulation is ECS significantly smaller than in all other simulations and the cloud optical depth feedback is the dominant cloud feedback. In all other simulations, the cloud optical depth feedback is weak and changes in cloud feedbacks associated with cloud amount and cloud-top pressure dominate the overall cloud feedback. However, apart from the simulation with only ice below 0 °C, differences in the overall cloud feedback are not translated into differences in ECS in our model. This insensitivity to the cloud feedback in our model is explained with compensating effects in the clear sky.

The chemistry–climate model ECHAM-HAMMOZ contains a detailed representation of tropospheric and stratospheric reactive chemistry and state-of-the-art parameterizations of aerosols using either a modal scheme (M7) or a bin scheme (SALSA). This article describes and evaluates the model version ECHAM6.3-HAM2.3-MOZ1.0 with a focus on the tropospheric gas-phase chemistry. A 10-year model simulation was performed to test the stability of the model and provide data for its evaluation. The comparison to observations concentrates on the year 2008 and includes total column observations of ozone and CO from IASI and OMI, Aura MLS observations of temperature, HNO3, ClO, and O3 for the evaluation of polar stratospheric processes, an ozonesonde climatology, surface ozone observations from the TOAR database, and surface CO data from the Global Atmosphere Watch network. Global budgets of ozone, OH, NOx, aerosols, clouds, and radiation are analyzed and compared to the literature. ECHAM-HAMMOZ performs well in many aspects. However, in the base simulation, lightning NOx emissions are very low, and the impact of the heterogeneous reaction of HNO3 on dust and sea salt aerosol is too strong. Sensitivity simulations with increased lightning NOx or modified heterogeneous chemistry deteriorate the comparison with observations and yield excessively large ozone budget terms and too much OH. We hypothesize that this is an impact of potential issues with tropical convection in the ECHAM model.

A new scheme for stratiform cloud microphysics has been implemented in the ECHAM6-HAM2 general circulation model. It features a widely used description of cloud water with two categories for cloud droplets and raindrops. The unique aspect of the new scheme is the break with the traditional approach to describe cloud ice analogously. Here we parameterize cloud ice by a single category that predicts bulk particle properties (P3). This method has already been applied in a regional model and most recently also in the Community Atmosphere Model 5 (CAM5). A single cloud ice category does not rely on heuristic conversion rates from one category to another. Therefore, it is conceptually easier and closer to first principles. This work shows that a single category is a viable approach to describe cloud ice in climate models. Prognostic representation of sedimentation is achieved by a nested approach for sub-stepping the cloud microphysics scheme. This yields good results in terms of accuracy and performance as compared to simulations with high temporal resolution. Furthermore, the new scheme allows for a competition between various cloud processes and is thus able to unbiasedly represent the ice formation pathway from nucleation to growth by vapor deposition and collisions to sedimentation. Specific aspects of the P3 method are evaluated. We could not produce a purely stratiform cloud where rime growth dominates growth by vapor deposition and conclude that the lack of appropriate conditions renders the prognostic parameters associated with the rime properties unnecessary. Limitations inherent in a single category are examined.

Aerosol–cloud interactions (ACIs) are uncertain and the estimates of the ACI effective radiative forcing (ERFaci) magnitude show a large variability. Within the Aerosol_cci project the susceptibility of cloud properties to changes in aerosol properties is derived from the high-resolution AATSR (Advanced Along-Track Scanning Radiometer) data set using the Cloud–Aerosol Pairing Algorithm (CAPA) (as described in our companion paper) and compared to susceptibilities from the global aerosol climate model ECHAM6-HAM2 and MODIS–CERES (Moderate Resolution Imaging Spectroradiometer – Clouds and the Earth's Radiant Energy System) data. For ECHAM6-HAM2 the dry aerosol is analysed to mimic the effect of CAPA. Furthermore the analysis is done for different environmental regimes. The aerosol–liquid water path relationship in ECHAM6-HAM2 is systematically stronger than in AATSR–CAPA data and cannot be explained by an overestimation of autoconversion when using diagnostic precipitation but rather by aerosol swelling in regions where humidity is high and clouds are present. When aerosol water is removed from the analysis in ECHAM6-HAM2 the strength of the susceptibilities of liquid water path, cloud droplet number concentration and cloud albedo as well as ERFaci agree much better with those of AATSR–CAPA or MODIS–CERES. When comparing satellite-derived to model-derived susceptibilities, this study finds it more appropriate to use dry aerosol in the computation of model susceptibilities. We further find that the statistical relationships inferred from different satellite sensors (AATSR–CAPA vs. MODIS–CERES) as well as from ECHAM6-HAM2 are not always of the same sign for the tested environmental conditions. In particular the susceptibility of the liquid water path is negative in non-raining scenes for MODIS–CERES but positive for AATSR–CAPA and ECHAM6-HAM2. Feedback processes like cloud-top entrainment that are missing or not well represented in the model are therefore not well constrained by satellite observations. In addition to aerosol swelling, wet scavenging and aerosol processing have an impact on liquid water path, cloud albedo and cloud droplet number susceptibilities. Aerosol processing leads to negative liquid water path susceptibilities to changes in aerosol index (AI) in ECHAM6-HAM2, likely due to aerosol-size changes by aerosol processing. Our results indicate that for statistical analysis of aerosol–cloud interactions the unwanted effects of aerosol swelling, wet scavenging and aerosol processing need to be minimised when computing susceptibilities of cloud variables to changes in aerosol.

Increased concentrations of aerosol can enhance the albedo of warm low-level cloud. Accurately quantifying this relationship from space is challenging due in part to contamination of aerosol statistics near clouds. Aerosol retrievals near clouds can be influenced by stray cloud particles in areas assumed to be cloud-free, particle swelling by humidification, shadows and enhanced scattering into the aerosol field from (3-D radiative transfer) clouds. To screen for this contamination we have developed a new cloud–aerosol pairing algorithm (CAPA) to link cloud observations to the nearest aerosol retrieval within the satellite image. The distance between each aerosol retrieval and nearest cloud is also computed in CAPA. Results from two independent satellite imagers, the Advanced Along-Track Scanning Radiometer (AATSR) and Moderate Resolution Imaging Spectroradiometer (MODIS), show a marked reduction in the strength of the intrinsic aerosol indirect radiative forcing when selecting aerosol pairs that are located farther away from the clouds (−0.28±0.26 W m−2) compared to those including pairs that are within 15 km of the nearest cloud (−0.49±0.18 W m−2). The larger aerosol optical depths in closer proximity to cloud artificially enhance the relationship between aerosol-loading, cloud albedo, and cloud fraction. These results suggest that previous satellite-based radiative forcing estimates represented in key climate reports may be exaggerated due to the inclusion of retrieval artefacts in the aerosol located near clouds.

One component of aerosol–cloud interactions (ACI) involves dust and marine stratocumulus clouds (MSc). Few observational studies have focused on dust–MSc interactions, and thus this effect remains poorly quantified. We use observations from multiple sensors in the NASA A-Train satellite constellation from 2004 to 2012 to obtain estimates of the aerosol–cloud radiative effect, including its uncertainty, of dust aerosol influencing Atlantic MSc off the coast of northern Africa between 45° W and 15° E and between 0 and 35° N. To calculate the aerosol–cloud radiative effect, we use two methods following Quaas et al. (2008) (Method 1) and Chen et al. (2014) (Method 2). These two methods yield similar results of −1.5 ± 1.4 and −1.5 ± 1.6 W m−2, respectively, for the annual mean aerosol–cloud radiative effect. Thus, Saharan dust modifies MSc in a way that acts to cool the planet. There is a strong seasonal variation, with the aerosol–cloud radiative effect switching from significantly negative during the boreal summer to weakly positive during boreal winter. Method 1 (Method 2) yields −3.8 ± 2.5 (−4.3 ± 4.1) during summer and 1 ± 2.9 (0.6 ± 1) W m−2 during winter. In Method 1, the aerosol–cloud radiative effect can be decomposed into two terms, one representing the first aerosol indirect effect and the second representing the combination of the second aerosol indirect effect and the semidirect effect (i.e., changes in liquid water path and cloud fraction in response to changes in absorbing aerosols and local heating). The first aerosol indirect effect is relatively small, varying from −0.7 ± 0.6 in summer to 0.1 ± 0.5 W m−2 in winter. The second term, however, dominates the overall radiative effect, varying from −3.2 ± 2.5 in summer to 0.9 ± 2.9 W m−2 during winter. Studies show that the semidirect effect can result in a negative (i.e., absorbing aerosol lies above low clouds like MSc) or positive (i.e., absorbing aerosol lies within low clouds) aerosol–cloud radiative effect. The semipermanent MSc are low and confined within the boundary layer. CALIPSO shows that 61.8 ± 12.6 % of Saharan dust resides above North Atlantic MSc during summer for our study area. This is consistent with a relatively weak first aerosol indirect effect and also suggests the second aerosol indirect effect plus semidirect effect (the second term in Method 1) is dominated by the semidirect effect. In contrast, the percentage of Saharan dust above North Atlantic MSc in winter is 11.9 ± 10.9 %, which is much lower than in summer. CALIPSO also shows that 88.3 ± 8.5 % of dust resides below 2.2 km the winter average of MSc top height. During summer, however, there are two peaks, with 35.6 ± 13 % below 1.9 km (summer average of MSc top height) and 44.4 ± 9.2 % between 2 and 4 km. Because the aerosol–cloud radiative effect is positive during winter, and is also dominated by the second term, this again supports the importance of the semidirect effect. We conclude that Saharan dust–MSc interactions off the coast of northern Africa are likely dominated by the semidirect effect.

Aerosol–cloud interactions continue to constitute a major source of uncertainty for the estimate of climate radiative forcing. The variation of aerosol indirect effects (AIE) in climate models is investigated across different dynamical regimes, determined by monthly mean 500 hPa vertical pressure velocity (ω500), lower-tropospheric stability (LTS) and large-scale surface precipitation rate derived from several global climate models (GCMs), with a focus on liquid water path (LWP) response to cloud condensation nuclei (CCN) concentrations. The LWP sensitivity to aerosol perturbation within dynamic regimes is found to exhibit a large spread among these GCMs. It is in regimes of strong large-scale ascent (ω500 −1) and low clouds (stratocumulus and trade wind cumulus) where the models differ most. Shortwave aerosol indirect forcing is also found to differ significantly among different regimes. Shortwave aerosol indirect forcing in ascending regimes is close to that in subsidence regimes, which indicates that regimes with strong large-scale ascent are as important as stratocumulus regimes in studying AIE. It is further shown that shortwave aerosol indirect forcing over regions with high monthly large-scale surface precipitation rate (> 0.1 mm day−1) contributes the most to the total aerosol indirect forcing (from 64 to nearly 100 %). Results show that the uncertainty in AIE is even larger within specific dynamical regimes compared to the uncertainty in its global mean values, pointing to the need to reduce the uncertainty in AIE in different dynamical regimes.

Stratocumulus clouds are important for climate as they reflect large amounts of solar radiation back into space. However they are difficult to simulate in global climate models because they form under a sharp inversion and are thin. A comparison of model simulations with the ECHAM6-HAM2 global aerosol climate model to observations, reanalysis and literature data revealed too strong turbulent mixing at the top of stratocumulus clouds and a lack of vertical resolution. Further reasons for cloud biases in stratocumulus regions are the too "active" shallow convection scheme, the cloud cover scheme and possibly too low subsidence rates. To address some of these issues and improve the representation of stratocumulus clouds, we made three distinct changes to ECHAM6-HAM2. With a "sharp" stability function in the turbulent mixing scheme we have observed, similar to previous studies, increases in stratocumulus cloud cover and liquid water path. With an increased vertical resolution in the lower troposphere in ECHAM6-HAM2 the stratocumulus clouds form higher up in the atmosphere and their vertical extent agrees better with reanalysis data. The recently implemented in-cloud aerosol processing in stratiform clouds is used to improve the aerosol representation in the model. Including the improvements also affects the anthropogenic aerosol effect. In-cloud aerosol processing in ECHAM6-HAM2 leads to a decrease in the anthropogenic aerosol effect in the global annual mean from −1.19 Wm−2 in the reference simulation to −1.08 Wm−2, while using a "sharp" stability function leads to an increase to −1.34 Wm−2. The results from the simulations with increased vertical resolution are diverse but increase the anthropogenic aerosol effect to −2.08 Wm−2 at 47 levels and −2.30 Wm−2 at 95 levels.

Nudging as an assimilation technique has seen increased use in recent years in the development and evaluation of climate models. Constraining the simulated wind and temperature fields using global weather reanalysis facilitates more straightforward comparison between simulation and observation, and reduces uncertainties associated with natural variabilities of the large-scale circulation. On the other hand, the forcing introduced by nudging can be strong enough to change the basic characteristics of the model climate. In the paper we show that for the Community Atmosphere Model version 5 (CAM5), due to the systematic temperature bias in the standard model and the sensitivity of simulated ice formation to anthropogenic aerosol concentration, nudging towards reanalysis results in substantial reductions in the ice cloud amount and the impact of anthropogenic aerosols on long-wave cloud forcing. In order to reduce discrepancies between the nudged and unconstrained simulations, and meanwhile take the advantages of nudging, two alternative experimentation methods are evaluated. The first one constrains only the horizontal winds. The second method nudges both winds and temperature, but replaces the long-term climatology of the reanalysis by that of the model. Results show that both methods lead to substantially improved agreement with the free-running model in terms of the top-of-atmosphere radiation budget and cloud ice amount. The wind-only nudging is more convenient to apply, and provides higher correlations of the wind fields, geopotential height and specific humidity between simulation and reanalysis. Results from both CAM5 and a second aerosol–climate model ECHAM6-HAM2 also indicate that compared to the wind-and-temperature nudging, constraining only winds leads to better agreement with the free-running model in terms of the estimated shortwave cloud forcing and the simulated convective activities. This suggests nudging the horizontal winds but not temperature is a good strategy for the investigation of aerosol indirect effects since it provides well-constrained meteorology without strongly perturbing the model's mean climate.

In this study we examine the performance of 31 global model radiative transfer schemes in cloud-free conditions with prescribed gaseous absorbers and no aerosols (Rayleigh atmosphere), with prescribed scattering-only aerosols, and with more absorbing aerosols. Results are compared to benchmark results from high-resolution, multi-angular line-by-line radiation models. For purely scattering aerosols, model bias relative to the line-by-line models in the top-of-the atmosphere aerosol radiative forcing ranges from roughly −10 to 20%, with over- and underestimates of radiative cooling at lower and higher solar zenith angle, respectively. Inter-model diversity (relative standard deviation) increases from ~10 to 15% as solar zenith angle decreases. Inter-model diversity in atmospheric and surface forcing decreases with increased aerosol absorption, indicating that the treatment of multiple-scattering is more variable than aerosol absorption in the models considered. Aerosol radiative forcing results from multi-stream models are generally in better agreement with the line-by-line results than the simpler two-stream schemes. Considering radiative fluxes, model performance is generally the same or slightly better than results from previous radiation scheme intercomparisons. However, the inter-model diversity in aerosol radiative forcing remains large, primarily as a result of the treatment of multiple-scattering. Results indicate that global models that estimate aerosol radiative forcing with two-stream radiation schemes may be subject to persistent biases introduced by these schemes, particularly for regional aerosol forcing.