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Scientific discourse and quality assurance can be improved by open-access (OA) publishing with public peer review and community discussion. Over 25 years, the viability of this approach has been proven by the interactive OA journal Atmospheric Chemistry and Physics (ACP) and 18 other journals published by the European Geosciences Union (EGU) and its scientific service provider Copernicus Publications. The success of the EGU journals reflects the benefits of community-driven, interactive OA publishing, including high scientific quality and impact, efficient self-regulation, low cost, and financial sustainability. Since 2001, the EGU has published over 50 000 journal articles, 60 000 preprints and 250 000 comments, utilizing and integrating different OA financing models (green, gold, diamond/platinum). The EGU journals with multi-stage open peer review are linked to the OA repository and interactive community platform EGUsphere and to the virtual scientific highlight magazine EGU Letters, integrating different levels of scientific communication and exchange. The EGU publications combine multiple features of open science, including different forms of open peer review and community evaluation with open-access, open data and open-source elements tailored to the needs and preferences of different disciplines. Indeed, the EGU pioneering approach to transparent peer review has spread to other leading publishers, including the Nature publishing group. We review the approach, achievements and future perspectives of interactive OA publishing (including transformative/institutional agreements and AI/ML tools) and its contribution to a universal epistemic web that captures the scientific discourse and comprehensively documents what we know, how well we know it and where the limitations are.

Large biases in climate model simulations of cloud radiative properties over the Southern Ocean cause large errors in modeled sea surface temperatures, atmospheric circulation, and climate sensitivity. Here, we combine cloud-resolving model simulations with estimates of the concentration of ice-nucleating particles in this region to show that our simulated Southern Ocean clouds reflect far more radiation than predicted by global models, in agreement with satellite observations. Specifically, we show that the clouds that are most sensitive to the concentration of ice-nucleating particles are low-level mixed-phase clouds in the cold sectors of extratropical cyclones, which have previously been identified as a main contributor to the Southern Ocean radiation bias. The very low ice-nucleating particle concentrations that prevail over the Southern Ocean strongly suppress cloud droplet freezing, reduce precipitation, and enhance cloud reflectivity. The results help explain why a strong radiation bias occurs mainly in this remote region away from major sources of ice-nucleating particles. The results present a substantial challenge to climate models to be able to simulate realistic ice-nucleating particle concentrations and their effects under specific meteorological conditions.

Stratocumulus-to-cumulus transitions are driven primarily by increasing sea-surface temperatures, with additional contributions from numerous interacting cloud-controlling factors. Understanding these interactions is important for improving the accuracy of cloud responses to changes in climate and other environmental factors in global climate models. Many studies have found lower-tropospheric stability dictates the transition time, while aerosol-focused studies found that aerosol concentration plays a key role via the drizzle-depletion mechanism. We consider the role of aerosol together with several other cloud-controlling factors representing a selection of the wider environmental conditions that affect drizzle in a clean to moderately polluted environment. A 34-member perturbed parameter ensemble of idealised large-eddy simulations with 2-moment cloud microphysics is used to train Gaussian process emulators (statistical representations) of the relationships between the factors and two properties of the transition: transition temporal length and average rain water path. We base the ensemble around a composite of trajectories in the Northeastern Pacific during summer. Using these emulators, parameter space can be densely sampled to visualise the joint and individual effects of the factors on the transition properties. We find that in the low-aerosol regime (< 200 cm−3) the transition time is most strongly affected by the aerosol concentration out of the factors considered here. Fast transitions, under 40 h, occur in this regime with high mean rain water path, which is consistent with a drizzle-depletion effect. In the high-aerosol regime, the inversion strength becomes more important than the aerosol concentration through the inversion's effect on entrainment and the deepening-warming decoupling mechanism.

Ice-nucleating particles (INPs) are known to affect the amount of ice in mixed-phase clouds, thereby influencing many of their properties. The atmospheric INP concentration changes by orders of magnitude from terrestrial to marine environments, which typically contain much lower concentrations. Many modelling studies use parameterizations for heterogeneous ice nucleation and cloud ice processes that do not account for this difference because they were developed based on INP measurements made predominantly in terrestrial environments without considering the aerosol composition. Errors in the assumed INP concentration will influence the simulated amount of ice in mixed-phase clouds, leading to errors in top-of-atmosphere radiative flux and ultimately the climate sensitivity of the model. Here we develop a global model of INP concentrations relevant for mixed-phase clouds based on laboratory and field measurements of ice nucleation by K-feldspar (an ice-active component of desert dust) and marine organic aerosols (from sea spray). The simulated global distribution of INP concentrations based on these two species agrees much better with currently available ambient measurements than when INP concentrations are assumed to depend only on temperature or particle size. Underestimation of INP concentrations in some terrestrial locations may be due to the neglect of INPs from other terrestrial sources. Our model indicates that, on a monthly average basis, desert dusts dominate the contribution to the INP population over much of the world, but marine organics become increasingly important over remote oceans and they dominate over the Southern Ocean. However, day-to-day variability is important. Because desert dust aerosol tends to be sporadic, marine organic aerosols dominate the INP population on many days per month over much of the mid- and high-latitude Northern Hemisphere. This study advances our understanding of which aerosol species need to be included in order to adequately describe the global and regional distribution of INPs in models, which will guide ice nucleation researchers on where to focus future laboratory and field work.

Severe hailstorms have the potential to damage buildings and crops. However, important processes for the prediction of hailstorms are insufficiently represented in operational weather forecast models. Therefore, our goal is to identify model input parameters describing environmental conditions and cloud microphysics, such as the vertical wind shear and strength of ice multiplication, which lead to large uncertainties in the prediction of deep convective clouds and precipitation. We conduct a comprehensive sensitivity analysis simulating deep convective clouds in an idealized setup of a cloud-resolving model. We use statistical emulation and variance-based sensitivity analysis to enable a Monte Carlo sampling of the model outputs across the multi-dimensional parameter space. The results show that the model dynamical and microphysical properties are sensitive to both the environmental and microphysical uncertainties in the model. The microphysical parameters lead to larger uncertainties in the output of integrated hydrometeor mass contents and precipitation variables. In particular, the uncertainty in the fall velocities of graupel and hail account for more than 65 % of the variance of all considered precipitation variables and for 30 %–90 % of the variance of the integrated hydrometeor mass contents. In contrast, variations in the environmental parameters – the range of which is limited to represent model uncertainty – mainly affect the vertical profiles of the diabatic heating rates.

Convective cloud systems in the maritime tropics play a critical role in global climate, but accurately representing aerosol interactions within these clouds persists as a major challenge for weather and climate modelling. We quantify the effect of ice-nucleating particles (INPs) on the radiative properties of a complex tropical Atlantic deep convective cloud field using a regional model with an advanced double-moment microphysics scheme. Our results show that the domain-mean daylight outgoing radiation varies by up to 18 W m−2 depending on the chosen INP parameterisation. The key distinction between different INP parameterisations is the temperature dependence of ice formation, which alters the vertical distribution of cloud microphysical processes. The controlling effect of the INP temperature dependence is substantial even in the presence of Hallett–Mossop secondary ice production, and the effects of secondary ice formation depend strongly on the chosen INP parameterisation. Our results have implications for climate model simulations of tropical clouds and radiation, which currently do not consider a link between INP particle type and ice water content. The results also provide a challenge to the INP measurement community, as we demonstrate that INP concentration measurements are required over the full mixed-phase temperature regime, which covers around 10 orders of magnitude.

Aerosol measurements over the Southern Ocean are used to constrain aerosol–cloud interaction radiative forcing (RFaci) uncertainty in a global climate model. Forcing uncertainty is quantified using 1 million climate model variants that sample the uncertainty in nearly 30 model parameters. Measurements of cloud condensation nuclei and other aerosol properties from an Antarctic circumnavigation expedition strongly constrain natural aerosol emissions: default sea spray emissions need to be increased by around a factor of 3 to be consistent with measurements. Forcing uncertainty is reduced by around 7 % using this set of several hundred measurements, which is comparable to the 8 % reduction achieved using a diverse and extensive set of over 9000 predominantly Northern Hemisphere measurements. When Southern Ocean and Northern Hemisphere measurements are combined, uncertainty in RFaci is reduced by 21 %, and the strongest 20 % of forcing values are ruled out as implausible. In this combined constraint, observationally plausible RFaci is around 0.17 W m−2 weaker (less negative) with 95 % credible values ranging from −2.51 to −1.17 W m−2 (standard deviation of −2.18 to −1.46 W m−2). The Southern Ocean and Northern Hemisphere measurement datasets are complementary because they constrain different processes. These results highlight the value of remote marine aerosol measurements.

The climatic relevance of aerosol–cloud interactions depends on the sensitivity of the radiative effect of clouds to cloud droplet number N, and liquid water path LWP. We derive the dependence of cloud fraction CF, cloud albedo AC, and the relative cloud radiative effect rCRE=CF⋅AC on N and LWP from 159 large-eddy simulations of nocturnal stratocumulus. These simulations vary in their initial conditions for temperature, moisture, boundary-layer height, and aerosol concentration but share boundary conditions for surface fluxes and subsidence. Our approach is based on Gaussian-process emulation, a statistical technique related to machine learning. We succeed in building emulators that accurately predict simulated values of CF, AC, and rCRE for given values of N and LWP. Emulator-derived susceptibilities ∂ln⁡rCRE/∂ln⁡N and ∂ln⁡rCRE/∂ln⁡LWP cover the nondrizzling, fully overcast regime as well as the drizzling regime with broken cloud cover. Theoretical results, which are limited to the nondrizzling regime, are reproduced. The susceptibility ∂ln⁡rCRE/∂ln⁡N captures the strong sensitivity of the cloud radiative effect to cloud fraction, while the susceptibility ∂ln⁡rCRE/∂ln⁡LWP describes the influence of cloud amount on cloud albedo irrespective of cloud fraction. Our emulation-based approach provides a powerful tool for summarizing complex data in a simple framework that captures the sensitivities of cloud-field properties over a wide range of states.

A 1200×1200 km2 area of the tropical South Atlantic Ocean near Ascension Island is studied with the HadGEM climate model at convection-permitting and global resolutions for a 10-day case study period in August 2016. During the simulation period, a plume of biomass burning smoke from Africa moves into the area and mixes into the clouds. At Ascension Island, this smoke episode was the strongest of the 2016 fire season.The region of interest is simulated at 4 km resolution, with no parameterised convection scheme. The simulations are driven by, and compared to, the global model. For the first time, the UK Chemistry and Aerosol model (UKCA) is included in a regional model with prognostic aerosol number concentrations advecting in from the global model at the boundaries of the region.Fire emissions increase the total aerosol burden by a factor of 3.7 and cloud droplet number concentrations by a factor of 3, which is consistent with MODIS observations. In the regional model, the inversion height is reduced by up to 200 m when smoke is included. The smoke also affects precipitation, to an extent which depends on the model microphysics. The microphysical and dynamical changes lead to an increase in liquid water path of 60 g m−2 relative to a simulation without smoke aerosol, when averaged over the polluted period. This increase is uncertain, and smaller in the global model. It is mostly due to radiatively driven dynamical changes rather than precipitation suppression by aerosol.Over the 5-day polluted period, the smoke has substantial direct radiative effects of +11.4 W m−2 in the regional model, a semi-direct effect of −30.5 W m−2 and an indirect effect of −10.1 W m−2. Our results show that the radiative effects are sensitive to the structure of the model (global versus regional) and the parameterization of rain autoconversion. Furthermore, we simulate a liquid water path that is biased high compared to satellite observations by 22 % on average, and this leads to high estimates of the domain-averaged aerosol direct effect and the effect of the aerosol on cloud albedo. With these caveats, we simulate a large net cooling across the region, of −27.6 W m−2.

The regulation introduced in 2020 that limits the sulfur content in shipping fuel has reduced sulfur emissions over global open oceans by about 80 %. This is expected to have reduced aerosols that both reflect solar radiation directly and affect cloud properties, with the latter also changing the solar radiation balance. Here we investigate the impacts of this regulation on aerosols and climate in the HadGEM3-GC3.1-LL climate model. The global aerosol effective radiative forcing caused by reduced shipping emissions is estimated to be 0.13 W m−2, which is equivalent to an additional ∼50 % to the net positive forcing resulting from the reduction in all anthropogenic aerosols from the late-20th century to the pre-2020 era. Ensembles of global coupled simulations from 2020–2049 predict a global mean warming of 0.04 K averaged over this period. Our simulations are not clear on whether the global impact is yet to emerge or has already emerged because the present-day impact is masked by variability. Nevertheless, the impact of shipping emission reductions either will have already committed us to warming above the 1.5 K Paris target or will represent an important contribution that may help explain part of the rapid jump in global temperatures over the last 12 months. Consistent with previous aerosol perturbation simulations, the warming is greatest in the Arctic, reaching a mean of 0.15 K Arctic-wide and 0.3 K in the Atlantic sector of the Arctic (which represents a greater than 10 % increase in the total anthropogenic warming since pre-industrial times).