Dataset for "Regional variability of aerosol impacts on clouds and radiation in global kilometer-scale simulations" by Herbert et al. (2025)

Atmos. Chem. Phys., 25, 7789–7814, 2025 https://doi.org/10.5194/acp-25-7789-2025 © Author(s) 2025. This work is distributed under the Creative Commons Attribution 4.0 License. Research article Regional variability of aerosol impacts on clouds and radiation in global kilometer-scale simulations Ross J. Herbert1,a, Andrew I. L. Williams1,b, Philipp Weiss1, Duncan Watson-Parris1,c,d, Elisabeth Dingley1,e, Daniel Klocke2, and Philip Stier1 1Atmospheric Oceanic and Planetary Physics, Department of Physics, University of Oxford, Oxford, UK 2Max Planck Institute for Meteorology, Hamburg, Germany anow at: Institute for Climate and Atmospheric Science, University of Leeds, Leeds, UK bnow at: Program in Atmospheric and Oceanic Science, Princeton University, Princeton, NJ, USA cnow at: Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA dnow at: Halıcıo˘ glu Data Science Institute, University of California San Diego, La Jolla, CA, USA enow at: CMIP International Project Office, ECSAT, Harwell Science and Innovation Campus, Didcot, UK Correspondence: Ross J. Herbert (r[email protected]) Received: 5 June 2024 – Discussion started: 10 June 2024 Revised: 9 May 2025 – Accepted: 13 May 2025 – Published: 23 July 2025 Abstract. Anthropogenic aerosols are a primary source of uncertainty in future climate projections. Changes to aerosol concentrations modify cloud radiative properties, radiative fluxes, and precipitation from the microphysical to the global scale. Due to computational constraints, we have been unable to explicitly simulate cloud dynamics in global-scale simulations, leaving key processes, such as convective updrafts, parameterized. This has significantly limited our understanding of aerosol impacts on convective clouds and climate. However, new state-of-the-art climate models are capable of representing these scales. In this study, we used the kilometer-scale ICOsahedral Nonhydrostatic (ICON) Earth system model to explore the global-scale rapid response of clouds and precipitation to an idealized distribution of anthropogenic aerosol via aerosol–cloud (ACI) and aerosol– radiation (ARI) interactions. In our simulations over 30d, we find that the aerosol impacts on clouds and precipitation exhibit strong regional dependence. The impact of ARI and ACI on clouds in isolation shows some consistent behavior, but the magnitude and additive nature of the effects are regionally dependent. Some regions are dominated by either ACI or ARI, whereas others behaved nonlinearly. This suggests that the findings of isolated case studies from regional simulations may not be globally representative; ARI and ACI cannot be considered independently and should both be interactively represented in modeling studies. We also observe pronounced diurnal cycles in the rapid response of cloud microphysical and radiative properties, which suggests that the usefulness of using polar-orbiting satellites to quantify ACI and ARI may be more limited than presently assumed. The simulations highlight some limitations that need to be considered in future studies. Isolating kilometer-scale aerosol responses from internal variability will require longer averaging periods or ensemble simulations. It would also be beneficial to use interactive aerosols and assess the sensitivity of the conclusions to the cloud microphysics scheme. Published by Copernicus Publications on behalf of the European Geosciences Union. 7790 R. J. Herbert et al.: Aerosol–cloud–radiation interactions in global kilometer-scale simulations 1 Introduction Aerosols and their impact on Earth’s climate remain a key uncertainty for anthropogenic climate change. On a global scale, they act primarily to cool the climate, partially compensating for warming induced by greenhouse gases (Bellouin et al., 2020; Forster et al., 2021; Watson-Parris and Smith, 2022). On the regional scale, they influence clouds, precipitation, and fluxes of radiation throughout the atmosphere. However, the magnitude of their global and regional impact remains uncertain (Gliß et al., 2021; Myhre et al., 2018; Sand et al., 2021; Williams et al., 2022). Aerosols modify the atmosphere via two pathways: aerosol–cloud (ACI) and aerosol–radiation (ARI) interactions. ACI considers the role that aerosols play in their ability to act as cloud condensation nuclei or ice-nucleating particles, thus directly influencing the distribution of cloud droplets or ice particles and modifying the radiative properties of clouds and precipitation processes. ARI considers the impact that aerosols have via their radiative properties on scattering and absorption, thereby modifying the fluxes of radiation at the surface and top of atmosphere (TOA) and the vertical heating profile. Both ACI and ARI can interact with cloud dynamics, leading to circulation and precipitation changes. Quantifying aerosol effects on global and regional climate is challenging due to the microphysical scales upon which ACI and ARI processes fundamentally act, which cannot be explicitly represented in global models, limiting our ability to accurately quantify their role in the present and future climates. An important source of uncertainty arises in the inability for models to sufficiently represent the turbulent motions that drive the vertical transport of energy and water, which has important implications for the formation and evolution of shallow and deep convective clouds, their diurnal cycle, and interactions between aerosols, the cloud-scale environment, and the large-scale environment. Current Earth system models (ESMs) use horizontal resolutions typically ranging from tens to hundreds of kilometers. On these scales, fundamental climate processes, such as mesoscale convective systems or ocean eddies, remain unresolved and need to be parameterized. This requires the use of convection parameterizations, introducing significant uncertainties due to structural limitations such as locality (Wang et al., 2022), lack of convective memory (Colin et al., 2019; Tan et al., 2018), or the inability to accurately represent convective organization and mesoscale convective systems (Mapes and Neale, 2011; Shamekh et al., 2023). ESMs also generally have very simplified representations of convective microphysics, as they do not explicitly represent vertical motions and associated cooling rates. A wide range of aerosol effects on convective clouds have been proposed that cannot be represented in the highly parameterized configurations of current ESMs. Regional highresolution models provide useful process insights (Marinescu et al., 2021), but their global representativeness remains unclear as the role of aerosols in one location may not be applicable to other regions (Williams et al., 2023). Regional simulations may also not adequately represent the interaction between the large-scale thermodynamic environment and the regional scale (Dagan et al., 2022). Previous work using limited area simulations with kilometer-scale resolution has shown that aerosols have the potential to significantly modify the diurnal cycle of convection and cloud evolution over widespread regions (Herbert et al., 2021a; Liu et al., 2020; Hodzic and Duvel, 2018). This is supported by observations showing that aerosol perturbations can significantly modify widespread properties of clouds through changes to the development and evolution of convection (Herbert and Stier, 2023; Jiang et al., 2018; Koren et al., 2004; Yu et al., 2007). A new generation of global kilometer-scale models is now being developed to run on scales that explicitly simulate convection – a significant step towards a more realistic representation of the Earth system (Palmer and Stevens, 2019). The DYnamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains (DYAMOND) initiative (Stevens et al., 2019) has brought together a number of these next generation models to explore their capabilities and has demonstrated that many dynamical features in the Earth system are better reproduced with resolved convection. As such, this has greatly improved the realism (Ban et al., 2021; Kendon et al., 2019) and predictive skill (Weber and Mass, 2019) of regional precipitation magnitudes and timings across the tropics and midlatitudes. It has also been shown to improve the representation of global-scale features such as the Madden–Julian Oscillation (Savarin and Chen, 2022), demonstrating the benefits of employing these models when studying global-scale teleconnections and patterns. Although much focus has been on convective processes and associated precipitation, the role of aerosols in these new configurations remains currently poorly understood. Many of the new-generation modeling frameworks include some representation of aerosol, though their role in the climate system has only been touched upon. Sato et al. (2018), for example, studied the warm-topped cloud liquid water path (LWP) response to perturbations of the aerosol optical depth (AOD) in the Non-hydrostatic ICosahedral Atmospheric Model (NICAM) model with 14 km horizontal resolution. The authors found that using an explicit representation of cloud microphysics on a global scale produced a negative LWP–AOD relationship, in agreement with satellite observations, which was not replicated in a coarser global model. The study demonstrates that ACI effects on a global-scale are sensitive to the representation of cloud processes but did not extend the analysis to other cloud types, nor consider ARI effects. It is well established that ARI can impact convective processes over land (Andreae et al., 2004; Bukowski and van den Heever, 2021; Herbert et al., 2021a; Hodzic and Duvel, 2018; Jiang et al., 2018; Koren et al., 2008; Park and van den Heever, 2022) and ocean Atmos. Chem. Phys., 25, 7789–7814, 2025 https://doi.org/10.5194/acp-25-7789-2025 R. J. Herbert et al.: Aerosol–cloud–radiation interactions in global kilometer-scale simulations 7791 (Gordon et al., 2018; Williams et al., 2022). Therefore, it is important to understand its role alongside the improved representation of convection in these new-generation models. Aerosols themselves are also a source of uncertainty in ESMs and high-resolution simulations due to complex aerosol microphysical processes that are poorly constrained or inadequately represented (White et al., 2017; Sand et al., 2021; Vogel et al., 2022; Regayre et al., 2018; Gliß et al., 2021). This complexity can also inhibit the interpretability of model behavior (Proske et al., 2023) and may not necessarily scale with improved model representation (Ekman, 2014). Previous studies have used idealized or simplified aerosol representations to remove this uncertainty and focus on quantifying aerosol interactions at the process level. Prescribed aerosol fields have been used to systematically quantify the sensitivity of the atmosphere to aerosol properties, including horizontal gradients (Lee et al., 2014), vertical profiles (Herbert et al., 2020; Johnson et al., 2004), concentrations (Dagan and Eytan, 2024; Tang et al., 2024), and spatial distributions (Williams et al., 2022; Dagan et al., 2021; Fiedler et al., 2017; Herbert et al., 2021a; Fiedler and Putrasahan, 2021). Idealized aerosol representations have also proven useful for identifying model structural uncertainties and estimating aerosol radiative forcing in intercomparison studies (Stier et al., 2013; Fiedler et al., 2019; Randles et al., 2013; Fiedler et al., 2023) and have been combined with reduced complexity climate models to provide a means of assessing sensitivity to future aerosol scenarios (Herbert et al., 2021b; Stjern et al., 2024; Recchia and Lucarini, 2023). The emergence of next-generation kilometer-scale ESMs provides a unique opportunity to study aerosol–convection interactions and the interactions with the large-scale environment. However, at least initially, the uncertainty in explicitly simulated aerosols will remain significant, making it difficult to disentangle the complex cloud response from differences in the aerosol representation. Therefore, in this study, we examine the impact of idealized anthropogenic aerosol perturbations on the climate using global storm-resolving simulations with the ICOsahedral Nonhydrostatic (ICON) model (Hohenegger et al., 2023) coupled to the simple plume implementation of the Max Planck Institute Aerosol Climatology version 2 (MACv2-SP; Stevens et al., 2017). We analyze the rapid response of clouds and the thermodynamic environment to an aerosol perturbation by contrasting simulations using aerosol representative of the pre-industrial era with aerosol representative of the present day. 2 Methodology 2.1 Model description and setup We use the ICON model in its Sapphire configuration, which is designed for kilometer-scale simulations of the Earth system. A detailed description and an evaluation are presented by Hohenegger et al. (2020, 2023) and only briefly described here. The atmosphere is solved with the non-hydrostatic model from Zängl et al. (2015), and land is represented with the Jena Scheme for Biosphere Atmosphere Coupling in Hamburg (JSBACH) dynamic vegetation model (Reick et al., 2013). We run the model in an atmosphere-only mode, with sea surface properties (sea surface temperature and sea ice concentration) prescribed as atmospheric boundary conditions following the atmospheric model intercomparison project AMIP (Taylor et al., 2012). The atmosphere is modeled with non-hydrostatic equations for the conservation of mass, momentum, and energy as well as parameterization schemes for the unresolved physical processes. The equations are discretized on an icosahedral-based mesh and integrated with a two-level predictor–corrector scheme. ICON includes parameterization schemes for radiation (Pincus et al., 2019), cloud microphysics (Baldauf et al., 2011), and turbulence (Smagorinsky, 1963). Turbulence is parameterized with the Smagorinsky scheme even though turbulent eddies are partially resolved at the kilometer scale (Dipankar et al., 2015; Hohenegger et al., 2023; Smagorinsky, 1963). Radiation is parameterized with a radiative transfer scheme from Pincus et al. (2019). The scheme computes radiative properties and radiative fluxes over 14 shortwave bands and 16 longwave bands. The optical properties of clouds are sensitive to the cloud droplet number concentration, Nd, which follows a predefined vertical profile and is discussed further in Sect. 2.2.1. Cloud microphysics are parameterized with the one-moment scheme from Baldauf et al. (2011). The scheme computes the masses of six hydrometeor classes: water vapor, cloud water, cloud ice, rain, snow, and graupel. The classes interact based on parameterized processes including condensation and autoconversion of cloud droplets to rain, the latter of which follows the description qaut ∼N−2 d(Seifert and Beheng, 2006). In our simulations, we use a horizontal resolution of approximately 5 km with 90 levels from the surface to 75 km corresponding to a vertical resolution of about 25 to 400m (Hohenegger et al., 2023, G_AO_5km setting). This configuration of ICON does not explicitly resolve the smallest scales of convection (<5km) but has been shown to reproduce many features of the climate system relevant for this study (Hohenegger et al., 2023), including seasonal cycles of precipitation and soil moisture, the structure of the atmosphere in deep convective regions, and coupling between sea surface temperature and precipitation. Segura et al. (2022) also demonstrate that this configuration reproduces the observed diurnal cycle of tropical precipitation. Given that ESMs tend to use spatial resolutions of tens to hundreds of kilometers, this makes a marked improvement in our ability to resolve many aspects of convection (Done et al., 2004; Prein et al., 2013) and is well suited for our study. The simulations are initialized using the ERA5 meteorological reanalysis and run for a 40 d period, similar to the DYAMOND protocol (Stevens et al., 2019), which includes a 10 d period of spin-up. The prescribed oceanic properties are fixed at mean https://doi.org/10.5194/acp-25-7789-2025 Atmos. Chem. Phys., 25, 7789–7814, 2025 7792 R. J. Herbert et al.: Aerosol–cloud–radiation interactions in global kilometer-scale simulations September values for the year 2016. The month of September is chosen due to the pronounced biomass burning activity that occurs in this month around the world (van der Werf et al., 2017). This provides us with a large global mean aerosol perturbation. The use of fixed, monthly mean sea surface temperatures and sea ice reduces the noise due to atmosphere– ocean coupling and allows us to focus on the rapid response of the atmosphere and climate to the aerosol perturbation, without the confounding effects of sea surface temperature changes. Aerosol perturbations, described in the following section, are held at mean September values for the year 2016 to produce a consistent aerosol perturbation throughout the simulations. 2.2 Aerosol representation In this study, natural aerosols are represented by the Max Planck Institute Aerosol Climatology version 2 (MACv2.0), described by Kinne (2019), which we will refer to as K19, and anthropogenic aerosols are represented using the simple plume implementation of MACv2.0, named MACv2-SP (Stevens et al., 2017). The K19 climatology and MACv2SP are used in ICON to represent aerosols in the radiation scheme. We extend MACv2-SP to the cloud microphysics scheme to link the anthropogenic aerosol perturbation to the warm-rain process (auto-conversion). The prescribed fields of aerosol are non-interactive, but magnitudes are spatially and temporally variable. This is a simplified representation but provides a means to robustly isolate the role of aerosols in the climate system (Fiedler et al., 2019, 2017) without the added complexity of aerosol microphysical processes, which are themselves an important source of uncertainty in ESMs and high-resolution simulations (White et al., 2017; Sand et al., 2021; Vogel et al., 2022; Regayre et al., 2018; Mann et al., 2014; Gliß et al., 2021). 2.2.1 Aerosol–radiation interactions ARI effects are included in the ICON radiation scheme. 3D fields of aerosol extinction from natural sources in the preindustrial era (year 1850) are taken from the K19 aerosol climatology described by Kinne (2019). Anthropogenic aerosol perturbations are represented using MACv2-SP, described in full by Stevens et al. (2017), which provides the model with 3D fields of aerosol extinction that are calculated for nine predefined plumes of aerosol concentrations and optical properties. The plumes are spatially consistent with the dominant sources of global anthropogenic aerosol emissions, and each is characterized by parameters that control its horizontal and vertical distribution, aerosol concentration and optical properties, annual cycle, and year-to-year variations. The plumes extend from the surface to the top of the model atmosphere and generally peak between 2 and 5 km. Each plume is representative of either industrial or biomass burning emissions, defined by the single-scattering albedo (0.93 or 0.87 at 500 nm) applied to the aerosol field. The plume aerosol concentrations are scaled year to year between 1850 and 2016, starting from 0.0 in 1850, to match the historical period. The contributions from the natural aerosol (K19) and anthropogenic aerosol (MACv2-SP) are summed to produce the prescribed fields of aerosol extinction in the ICON radiation scheme. In our configuration of MACv2-SP, we adjust the biomass burning plumes (North Africa, South America, Southeast Asia, and South Central Africa). In the standard MACv2-SP setup for the present-day climate, anthropogenic sources account for around 40 % of the plume extinction. These figures are uncertain (Hamilton et al., 2018) and may substantially underestimate the anthropogenic contribution (Lauk and Erb, 2009). In our simulations, we enhance the anthropogenic contribution in MACv2-SP by a factor of 1.5, which increases the anthropogenic contribution to around 50 %. This is consistent with higher estimates (Lauk and Erb, 2009, and references therein) and should provide a stronger signal in response to our perturbations. As we show in Fig. 1, the resulting distribution and magnitude of present-day AOD are consistent with observations. 2.2.2 Aerosol–cloud interactions ACI effects are included in the ICON radiation scheme (cloud optical properties) and cloud microphysics scheme (autoconversion rate) using global distributions of AOD perturbations provided by MACv2-SP. The two schemes are not coupled and employ different assumptions about the cloud droplet number concentration (Nd). Therefore we use the variable names Nd,rad and Nd,cld to distinguish between the treatment of Ndin the two schemes. In the radiation scheme, the vertical profile of cloud droplet effective radius is dependent on the cloud water content, the cloud droplet number concentration Nd,rad, and a scaling factor that accounts for the width of the droplet distribution (Stevens et al., 2013, Eq. 7). Nd,rad follows a predefined profile in the radiation scheme Nd,rad(p)=   Nd,rad-top +Nd,rad-sfc −Nd,rad-top exp1−(p/800hPa)2, p < 800hPa Nd,rad-sfc,else (1) where Nd,rad-top and Nd,rad-sfc are the number concentration at the top and bottom of the atmosphere and pis the pressure. In the default ICON configuration Nd,rad-top is set to 20cm−3 and Nd,rad-sfc is set to 120 cm−3over land and 80 cm−3over oceans. These are the values we use for the pre-industrial climate. For ACI effects in the present-day climate we use a spatially dependent ACI scaling factor, fN, that modifies the global distribution of Nd,rad-sfc (Nd,rad-top is kept constant at 20 cm−3). Following the approach of Stevens et al. (2017, Eq. 15), fNis calculated in MACv2-SP using fN=ln(bNτPD +1) ln(bNτPI +1),(2) Atmos. Chem. Phys., 25, 7789–7814, 2025 https://doi.org/10.5194/acp-25-7789-2025 R. J. Herbert et al.: Aerosol–cloud–radiation interactions in global kilometer-scale simulations 7793 Figure 1. Climatologies of satellite-retrieved (a) September mean AOD550nm (for 2010–2015) measured by Platnick et al. (2015) and (b) annual mean Ndfor cloud tops <3.2 km (2003–2015; only showing grid points with 50 successful retrievals) estimated by Grosvenor et al. (2018). Panel (c) shows the simulated AOD from the K19 climatology and plume model MACv2-SP (top row) and Nd,cld (bottom row) in each of the four simulations. where τPI and τPD are the AOD in the pre-industrial and present-day climates and bNis a predefined parameter that describes the sensitivity of Ndto AOD (Nd,cld =aNln(bNτ+ 1); Stevens et al., 2017). In the default MACv2-SP setup, aNand bNhave values of 60 cm−3and 20. This provides a relatively weak sensitivity (Nd,cld =140 cm−1for τ=0.5), which may be inconsistent with observations over land (Hudson and Yum, 2001; McCoy et al., 2018; Miles et al., 2000; Squires, 1958) and in the presence of convective updrafts (Braga et al., 2021; Gryspeerdt et al., 2023; Machado et al., 2018; Pringle et al., 2009), showing concentrations in excess of 300 cm−3. In this study, we set the values of aNand bNto 410 cm−3and 5, taken from Herbert et al. (2021a). This provides more sensitivity than the original but, as we show in Fig. 1, results in a present-day distribution of Ndconsistent with observations. In the default ICON setup, the microphysics scheme uses a predefined value for the cloud droplet number concentration (Nd,cld) that is spatially invariable and constant in altitude. We use this for our PI distribution of Nd,cld, which we set to 80 cm−3. We represent ACI effects in the microphysics scheme using the ACI scaling factor fN, as calculated above. Applying fNto the pre-industrial distribution of Nd,cld provides an idealized present-day distribution that is spatially consistent with the anthropogenic contributions in the MACv2-SP plumes. 2.3 Simulations We use four simulations to explore the rapid response of clouds and climate to our idealized aerosol perturbations (outlined in Table 1). The control simulation (PI) uses values that are representative of a pre-industrial atmosphere consisting of natural aerosol and background ARI and ACI effects. Global fields of natural aerosol extinction are represented by the K19 climatology for the year 1850. Nd,cld is held constant at a value of 80 cm−3, whilst Nd,rad follows a vertical profile according to Eq. (1) and varies spatially with Nd,rad-sfc set to 120 cm−3on land and 80 cm−3over oceans. A second simulation (PD) is run with values that are representative of a present-day atmosphere that includes ACI and ARI effects due to anthropogenic activity. Aerosol extinction fields from anthropogenic aerosol are represented by the plume model MACv2-SP for the year 2016 and added to the pre-industrial contribution (Fig. S1 in the Supplement shows the spatial distribution of the anthropogenic AOD perturbation). The spatial distributions of Nd,cld and Nd,rad are modified using the scaling factor fN(Eq. 2), which varies spatially with the anthropogenic aerosol. The third and fourth simulations are used to isolate ACI and ARI effects in the present-day atmosphere. In the third simulation, PDARI, extinction from the anthropogenic aerosols is included, but the scaling factor fN is not applied to Nd,cld and Nd,rad; this isolates ARI effects associated with anthropogenic aerosol. In the final simulation, PDACI, the ACI scaling factor fNis applied, but aerosol extinction remains at pre-industrial values; this isolates ACI effects associated with anthropogenic aerosol. Figure 1 shows that the simulated spatial distributions of AOD and Nd,cld are consistent with present-day observations. In the PD run, the aerosol perturbations are centered over regions with pronounced industrial emissions of sulfate (South and East Asia, North America, and Europe) and biomass burning emissions from agricultural activities in heavily forested regions in the Southern Hemisphere (South America, South Central Africa, and the Maritime Continent). Nd,cld in the PD run reaches maximum concentrations of about 320 cm−3over East Asia. The spatial distribution and range is consistent with present-day climatologies presented by Grosvenor et al. (2018) and McCoy et al. (2018), who report Nd,cld values exceeding 300 cm−3over East Asia https://doi.org/10.5194/acp-25-7789-2025 Atmos. Chem. Phys., 25, 7789–7814, 2025 7794 R. J. Herbert et al.: Aerosol–cloud–radiation interactions in global kilometer-scale simulations Table 1. Description of each simulation. Simulation name ARI characteristics ACI characteristics PI (pre-industrial) Aerosol extinction fields for natural aerosol only, represented by the K19 climatology for the year 1850 (K19 only). Pre-industrial magnitudes of Nd,cld and Nd,rad.Nd,cld is spatially constant with a value of 80 cm−3.Nd,rad follows a vertical profile according to Eq. (1) and varies spatially with Nd,rad-sfc set to 120 cm−3over land and 80 cm−3over oceans. PD (present-day) Aerosol extinction fields include anthropogenic contribution, represented by the plume model MACv2-SP for the year 2016 (K19 +MACv2-SP). Present-day magnitudes of Nd,cld and Nd,rad. Global distributions of Nd,cld and Nd,rad-sfc increased by spatially variable ACI scaling factor fN, as described by Eq. (2). PDARI (present-day; isolate ARI) PD aerosol extinction: global fields of aerosol extinction follow the PD simulation (K19 +MACv2-SP). PI Nd,cld and Nd,rad. Global distributions of Nd,cld and Nd,rad-sfc follow the PI simulation. PDACI (present-day; isolate ACI) PI aerosol extinction: global fields of aerosol extinction follow the PI simulation (K19 only). PD Nd,cld and Nd,rad. Global distributions of Nd,cld and Nd,rad-sfc follow the PD simulation and are enhanced by fN. and around 200 cm−3off the coasts of the industrial regions of Asia (Fig. 1b). Elevated values are also evident over the Southeast Atlantic Ocean downwind of the African biomass burning regions. A comparison between simulated and observed Ndyields a root mean square error (RMSE) of 49 cm−3and a correlation coefficient of 0.57 (the default parameters aNand bNyield an RMSE of 70 cm−3and correlation coefficient of 0.42). The discrepancy is in part due to high simulated values over biomass burning regions that are not reflected in annual mean observations but also due to regional variability that MACv2-SP does not capture (e.g., North America). Despite the relatively poor correlation, our idealized representation of aerosols provides appropriate perturbations to the radiative fluxes and bulk cloud properties that are spatially consistent with the dominant sources of global anthropogenic aerosol forcing. Snapshots of the change in total water path (TWP) due to the aerosol perturbation (PD −PI) from four time periods during the simulations are shown in Fig. 2. The limited length of our simulations poses some issues, as it is difficult to disentangle internal variability from the global-scale responses to aerosol effects. By internal variability, we refer to the chaotic nature of the atmosphere, in which small fluctuations grow rapidly in time. For example, Fig. 2 shows that as the simulation progresses, the changes in aerosol concentration have large-scale impacts on the precise timing and location of atmospheric fronts, which appear as a regional change when differencing simulations but are not usefully considered as a robust “aerosol effect”. This behavior is similar to initial condition sensitivity where small-scale perturbations at the beginning of the simulation can quickly develop into pronounced changes (Keshtgar et al., 2023; Lorenz, 1963). Figure 2. Snapshots of day 2, 5, 10, and 20 at 12:00 UTC after the initialization: total water path (TWP) in the PI simulation (left column) and TWP response (PD −PI) to the aerosol perturbation (right column). Estimating the radiative forcing due to anthropogenic aerosol on a global scale requires multi-year simulations that can robustly separate the response (signal) from interAtmos. Chem. Phys., 25, 7789–7814, 2025 https://doi.org/10.5194/acp-25-7789-2025 R. J. Herbert et al.: Aerosol–cloud–radiation interactions in global kilometer-scale simulations 7795 nal variability as in, e.g., the Coupled Model Intercomparison Project (CMIP) experiments (Schulz et al., 2006). Even longer durations are required to estimate the effective radiative forcing (Boucher et al., 2013). Hence, we do not focus on quantifying the global impact of aerosols on climate and instead focus on the impact of our aerosol perturbation on the regional scale, simultaneously for all regions of the world. We exploit the capability of the model to represent scales that are traditionally used by high-resolution simulations (∼5 km). These are used by the scientific community to focus on aerosol impacts to radiative fluxes and cloud processes across cloud to regional scales (tens to hundreds of kilometers) and typically run for days to weeks (e.g., ArcherNicholls et al., 2016; Ban et al., 2021; Che et al., 2021; Dagan et al., 2020; Fan et al., 2013; Marinescu et al., 2021; Heever et al., 2006; Liu et al., 2020; Storer et al., 2010; Takeishi and Wang, 2022). To study aerosol impacts on the global scale, we subset the outputs from the global simulations into 15° ×15° regions, producing the equivalent of 288 regional-scale simulations running for a 30d period. With this method, the regions can interact with each other, and any regional aerosol response is transported to neighboring regions. The power of this configuration is the ability to isolate the different pathways through which aerosols interact with the cloud and atmosphere for a wide range of thermodynamic states and boundary conditions. We can also identify consistent cloud-scale impacts across the globe without the uncertainty (from, e.g., different models, parameterizations, schemes, and time periods) that is introduced when traditionally collating simulation data on the spatial variability of aerosol impacts. 2.4 Temporal decomposition of regional response Several recent studies have identified pronounced aerosol effects on clouds and their properties occurring throughout the diurnal cycle (Herbert et al., 2021a; Herbert and Stier, 2023; Hodzic and Duvel, 2018). Therefore, we quantify the regional responses of clouds to the aerosol perturbation over the full diurnal cycle and also the daily mean effect. Data from the 5 km resolution output are re-gridded onto a regular 1° grid using the Climate Data Operators (CDO; http://www.idris.fr/media/ada/cdo.pdf, last access: 20 January 2025) software operator gencon, which generates firstorder conservative remapping weights. As we focus on regional domains, we do not lose any information through the re-gridding process. We attempt to isolate the responses due to the aerosol perturbation from internal variability and noise by temporally decomposing the mean time series into shortand long-term components and compositing onto a single diurnal cycle. A seasonal-trend decomposition tool is applied to the response time series (PD −PI) using LOESS (locally estimated scatterplot smoothing) based on Cleveland (1979), providing long-term, short-term, and residual components. LOESS is a statistical decomposition tool that can be applied to extract responses occurring on relatively high frequencies (e.g., diurnal) and has been used in previous climate-focused studies (Deng and Fu, 2019; Carslaw, 2005; Verbesselt et al., 2010; He et al., 2022; Liu and Zhang, 2024; Zhou et al., 2015; Cleveland, 1979; da Silveira Bueno et al., 2024; Papacharalampous et al., 2018; Quan et al., 2016; Jaber and Abu-Allaban, 2020; Rabbi and Kovács, 2024; Moradi, 2022; Deng et al., 2015). Examples of the decomposition for the Congo basin (0° N, 20° E) and the Southeast Atlantic Ocean (10° S, 5° W) are shown in Fig. 3. The short-term component (using an applied periodicity of 1 d) captures aerosol effects on a diurnal timescale, whereas the long-term component captures the internal variability combined with any persistent change. Examples of a persistent change may be a relatively warmer troposphere or enhanced subsidence and may represent an important local or non-local aerosol effect; hence we attempt to recapture this using a second application of the decomposition tool with a prescribed periodicity of 100 d. This provides a time-independent response over the time series, which we attribute to an aerosol effect. This method assumes that any internal variability is evenly distributed around the time-independent response, which may not be true but provides a reasonable approximation and should capture regions where strong persistent responses occur; we demonstrate our technique using synthetic data in the Supplement (Sect. S2). Recapturing the persistent aerosol effect is well demonstrated in the Southeast Atlantic region (Fig. 3e–h). Here, ACI strongly enhances the LWP of the extensive underlying marine stratocumulus resulting in a persistent positive LWP response with an overlying diurnal cycle. We further reduce the impact of internal variability and noise by compositing the short-term (diurnal) and long-term (persistent) aerosol effects onto a single diurnal cycle. 3 Results 3.1 Global-scale analysis In this section, we focus on the regional responses of clouds and radiative fluxes due to the aerosol perturbations across the globe. We focus on regions where we can robustly identify a response, which is achieved using the following criteria for each variable X. To remove regions that have transient synoptic-scale weather (e.g., a mid-latitude cyclone), the regional mean standard deviation of hourly PI values over the time series, P24 hr=1σ(XPI)hrNday−1, must be within the lowest 50th percentile of the global distribution. This isolates regions that exhibit a consistent diurnal cycle during the PI experiment. The regions where Xis likely unimportant are removed when the time series mean in the PI experiment, XPI, is in the lowest 25th percentile globally. Finally, we focus our analysis on regions where the response is more pronounced by removing those where the maximum range of the diurnal response, 1Xhrmax −1Xhrmin, normalized by XPI, is in the lowest 10th percentile globally. We determine the dominant https://doi.org/10.5194/acp-25-7789-2025 Atmos. Chem. Phys., 25, 7789–7814, 2025 7796 R. J. Herbert et al.: Aerosol–cloud–radiation interactions in global kilometer-scale simulations Figure 3. Example of the response time series decomposition at two locations: Congo (a–d) and Southeast Atlantic Ocean (e–h). The different colors represent 1LWP (PD −PI) for each PD simulation. The panels show the original time series before decomposition (a, e), the decomposed long-term component (b, f), the decomposed short-term component (c, g), and the residual (d, h). The horizontal dashed lines (in (b) and (f)) show the persistent aerosol effect that is added to the short-term aerosol effect. driver of the aerosol effect by calculating the RMSE between the responses from the PD and PDARI or PDACI simulations. The difference between the two is used to estimate whether one driver (ARI or ACI) dominates or whether both play a role. We start by focusing on the responses of LWP (Fig. 4) and precipitation (P; Fig. 5) to the aerosol perturbations. The figures demonstrate considerable spatial variability in the magnitude, direction, and driver of the aerosol effects on clouds. There is no consistent daily mean regional response in either LWP or P. The percentage increase in the magnitude of 1LWP varies from 10 %–50 %, with higher values close to or downstream of the aerosol perturbations. The magnitude is not consistently dependent on the aerosol perturbation, which is particularly evident over the Maritime Continent. This is in contrast to 1P , which tends to be spatially consistent with the aerosol perturbation and of similar magnitudes in all regions (>45%). The direction of the change is also inconsistent; 1LWP tends to be positive over the ocean and negative over the land, whilst 1P is negative in all regions except the Maritime Continent and West Pacific Ocean. In our model configuration, LWP and Pare linked via autoconversion (Sect. 2.1); therefore it is surprising that there is no clear consistency between the responses of the two cloud properties. Figures 4d–f and 5d–f suggest that the spatial inconsistency in the regional responses is attributable to the lack of consistent underlying aerosol effects. ACI tends to dominate 1LWP over the ocean, and ARI tends to dominate 1LWP over land. However, there are only a small number of regions in which the daily mean 1LWP is fully explained by either of the drivers. Individually, ARI and ACI become more pronounced when 1LWP is separated into day/night periods, in particular, for ARI in the daytime over Central Africa and East Asia. This suggests that in some regions ACI and ARI are more or less active during different periods of the diurnal cycle. The response of Pshows similar behavior, with both ACI and ARI influencing the daily mean. However, in contrast to LWP, ARI tends to be the main driver of 1P on the global scale. One source of spatial consistency is the range of the LWP and Presponses during the diurnal cycle. 1LWP (Fig. 4c) and 1P (Fig. 5c) range between ∼10–20 g m−2for LWP and 1–2 mm d−1for P. This is a consistent feature in all regions. In some (e.g., the Congo basin and Amazon rainforest) the daily mean response is small but the diurnal range is large, indicating contrasting periods of negative and positive responses during the day. Hence, the daily mean aerosol effect masks the underlying diurnal response. This is further explored in Fig. 6, where min/max 1LWP and its drivers (ARI/ACI) are shown for the diurnal response (Fig. 6a–b) and with the addition of the persistent response (Fig. 6c–d). All regions exhibit a marked diurnal range, particularly over land and close to the aerosol perturbations. ARI drives most of the range in the regions closest to the perturbations, which demonstrates that the impact of aerosol on clouds has a diAtmos. Chem. Phys., 25, 7789–7814, 2025 https://doi.org/10.5194/acp-25-7789-2025 R. J. Herbert et al.: Aerosol–cloud–radiation interactions in global kilometer-scale simulations 7797 Figure 4. Mean diurnal response of liquid water path (LWP) to the aerosol perturbation (PD −PI) from each 15°×15° region. Panels (a)–(c) show the diurnal magnitude of the response as a percentage (a), absolute daily mean (b), and absolute daily minimum/maximum (c). A larger circle size in panels (a)–(c) represents a location with an increasingly consistent diurnal cycle throughout the PI simulation. Panels (d)–(f) show the dominating aerosol effect (ARI/ACI) driving the LWP response during the diurnal cycle (d), day (e), and night (f). A larger circle size in panels (d)–(f) represents a better match between the individual response (PDARI or PDACI) and total response (PD). All panels show the AOD perturbation as contour lines at 0.05 increments. Figure 5. Mean diurnal response of precipitation (P) to the aerosol perturbation (PD −PI). Figure details as Fig. 4. https://doi.org/10.5194/acp-25-7789-2025 Atmos. Chem. Phys., 25, 7789–7814, 2025 7804 R. J. Herbert et al.: Aerosol–cloud–radiation interactions in global kilometer-scale simulations Figure 13. Same as Fig. 11 but for the Maritime Continent region. The response of the cloud field over the Maritime Continent (Fig. 13) is consistent with the other convective regions, but in addition, the aerosol perturbation impacts the large-scale circulation. A persistent aerosol effect in this region (and general absence over land) was identified in Fig. 6. This is evident in the decomposed diurnal cycles of 1LWP (Fig. 10c) and 1P (Fig. 13c), which exhibit a largely timeindependent response combined with an additional response in the early afternoon. The latter is consistent with the impacts to the convective environment as observed over the Amazon and the Congo, while the former is a modification to the large-scale circulation. ARI drives a persistent positive 1Mflux of ∼20%, primarily due to enhanced ascent throughout the column (Fig. 13g). The response of the largescale circulation due to ARI is consistent with the strengthening of the Walker Circulation and tropical ascent reported by Williams et al. (2022), where the anomalous source of diabatic heating projects onto the ascending branch of the Walker circulation. The global-scale analysis of 1Mflux in Fig. S3 shows a negative response over the Western Indian Ocean, which supports this hypothesis. ACI also drives a persistent increase in W∗but only above 6 km. This occurs alongside an increase in IWP and θ, which suggests a role for direct modification of the convective cloud cores via convective invigoration from the cold phase (Heever et al., 2006; Fan et al., 2013). This was not observed over the Congo or Amazon, which is consistent with Khain et al. (2008), who found that convective invigoration occurred in moist maritime deep convection but did not in drier, continental, deep convection. The sensitivity of deep convection in this region is consistent with regional modeling studies (Lee and Wang, 2020; Takeishi and Wang, 2022; Chang et al., 2024), yet there is no agreement on the sign or magnitude of the response. Chang et al. (2024) demonstrate that it is likely linked to the large-scale convective environment influenced by El Niño conditions. However, none of these studies report a persistent increase due to changes in the large-scale circulation, which may be due to the inability of these model configurations to represent the large-scale dynamical feedback that we simulate. The overall response of the Maritime Continent to the aerosol perturbations is driven by both ARI and ACI, with some properties of the cloud and atmosphere dominated by one of the pathways. The diurnal cycles of LWP, Mflux, and Pare approximately a linear sum of the contributions from the two pathways. During the a.m. period the LWC profile response is controlled by ACI effects, whilst during the p.m. time period both ARI and ACI contribute to the changes – illustrating the connection between ARI and deep convection over the land. ARI dominates the response of W∗in the warm-phase regions of the cloud (up to 5 km), whilst both ARI and ACI are active in the ice-phase regions. This highlights that the Maritime Continent may be particularly sensitive to anthropogenic aerosol due to its position within the Walker circulation and the pronounced diurnal cycle of convection. It is possible that other regions within ascending or descending branches of global atmospheric circulation may exhibit similar sensitivity (Williams et al., 2022) and should be considered in future studies. 4 Conclusions In this study we make the first steps towards investigating the impact of anthropogenic aerosol on clouds, precipitation, Atmos. Chem. Phys., 25, 7789–7814, 2025 https://doi.org/10.5194/acp-25-7789-2025 R. J. Herbert et al.: Aerosol–cloud–radiation interactions in global kilometer-scale simulations 7805 and radiation in a global kilometer-scale configuration of the ICON model. We focus on the rapid response of cloud and climate to a prescribed global aerosol perturbation, which we represent using the MACv2-SP plume model. We ran simulations for the month of September, for both pre-industrial (1850) and present-day (2016) aerosol distributions, providing a realistic range of anthropogenic perturbations across the globe. Additional PD simulations were run to isolate the role of ARI and ACI. In an effort to isolate the aerosol impacts from internal variability, we subset the globe into defined regions and temporally decompose the time series into diurnal and persistent components, which we composite onto a single diurnal cycle. In a global-scale analysis we subset the global simulation outputs into 15° ×15° regions, producing the equivalent of 288 regional high-resolution simulations that can interact with each other. We then focus on the regional-scale response at a process level in six locations heavily influenced by the aerosol perturbation. The global-scale analysis demonstrates considerable spatial variability in the magnitude, direction, and driver of the cloud responses to aerosol perturbations. A focus on 1LWP and 1P shows no consistent daily-mean regional response, and whilst 1P correlates with the aerosol perturbation, 1LWP does not consistently. The spatial variability is consistent with ARI and ACI effects playing regiondependent roles that are sensitive to the regional thermodynamic environment. Regional responses are rarely fully explained by one pathway, suggesting ARI and ACI both contribute to the total aerosol effect and must both be taken into account. The spatial variability in how clouds respond to the aerosol perturbation results in associated variability in the TOA shortwave radiative forcing. We have simulated the month of September when biomass burning emissions peak; therefore, we anticipate the spatial distribution of the forcing to be sensitive to the annual cycle. The sensitivity of 1LWP to aerosol consistently includes a diurnal component, which may be masked by the dailymean response. The diurnal range in 1LWP was greatest over land and close to the aerosol perturbation, demonstrating that the impact of aerosols on clouds has a diurnal driver that may be dependent on inherent regional diurnal cycles of clouds, dynamics, or solar radiation. The LWP response also includes a persistent increase that was stronger over oceans than on land. On the global scale, and for the regions that we could isolate a response from the aerosol perturbation, ARI tended to dominate 1LWP on the diurnal cycle and ACI dominated the persistent LWP increase. The pronounced diurnal cycle in LWP sensitivity to aerosol suggests that polarorbiting remote-sensing platforms, such as those on the ATrain constellation, may struggle to estimate climate-relevant responses of clouds and climate to aerosol as they only observe a limited period of the diurnal cycle at any one latitude. A focus on regions impacted by the aerosol perturbations shows some consistent process-level responses. Three regions, characterized by deep convection and emissions of biomass burning aerosol, consistently demonstrated a suppression of the diurnal cycle of convection via modifications to the convective environment due to ARI and enhanced LWP due to ACI. However, the combined effect (ARI+ACI) differed in each region. The direct modification to convective clouds (suppression or invigoration) via ACI also differed between regions. We hypothesize that the differences are a result of the large-scale thermodynamic environment unique to each region, manifesting as thermodynamic state dependence in the response to the aerosol. Large-scale responses were evident in the Indo-Pacific warm pool region in the ascending branch of the Walker circulation, driving persistent changes to the large-scale circulation alongside the diurnal cloud-scale response. The global-scale and regionalscale analyses point towards strong regional dependence in the impact of aerosols on clouds and climate; hence the outcomes from isolated case studies are likely not representative for other regions. The results also strongly suggest that ACI and ARI cannot be considered independently as the cloud responses via each pathway do not tend to be additive. Some were dominated by either ACI or ARI, and some behaved nonlinearly, resulting in a combined aerosol effect at odds with the individual components. In regions not directly influenced by the aerosol perturbation (e.g., remote regions like the Arctic), the decomposition method is unable to sufficiently isolate the cloud responses from internal variability. An extension of this analysis to the entire globe could be achieved via longer simulations (e.g., Bolot et al., 2023; Cheng et al., 2022; Sato et al., 2018) or ensembles (e.g., Deser et al., 2020; Dittus et al., 2020). However, this will require considerable computing resources. Sato et al. (2018) ran a year-long global kilometer-scale simulation using the NICAM model and analyzed ACI by focusing on the global relationship between LWP and the aerosol number concentration, removing the need to run multiple simulations. An alternative is to nudge the simulation to observed meteorology (e.g., Atlas et al., 2022, 2024; Terai et al., 2020). However, this will suppress any large-scale modifications, which our results suggest may be an important feature in some regions. The idealized representation of aerosol and Ndin this model has helped identify important process-level interactions and provides a platform for future studies using realistic aerosol perturbations. The use of non-interactive aerosol may mask important feedbacks and processes including the impact of clouds and precipitation on the spatio-temporal distribution of aerosols, changes to the surface properties and energy fluxes, and turbulence that would influence emissions and aerosol removal processes. Changes in aerosol concentrations would also affect Ndconcentrations and vertical profiles. Aerosol emissions also exhibit diurnal cycles (Yu et al., 2021; Torres and Ahn, 2024) that we do not account for. Future studies should also consider building on the temporal decomposition method (Sect. 2.4) as not all internal variability can be isolated from the aerosol-driven response. https://doi.org/10.5194/acp-25-7789-2025 Atmos. Chem. Phys., 25, 7789–7814, 2025 7806 R. J. Herbert et al.: Aerosol–cloud–radiation interactions in global kilometer-scale simulations The method assumes that mean internal variability during the time series is equal to zero; whilst this may be true on very long timescales (years to decades), it is unlikely to be the case over our simulation duration. The method additionally assumes that the persistent response due to the aerosol perturbation is independent of time. In reality, this component may increase or decrease during the simulation due to local or non-local feedbacks between clouds, the surface, and the thermodynamic properties of the region. This could be explored in future studies with longer simulations. Additional sources of uncertainty arise from the cloud microphysics scheme and unresolved convection. The choice of cloud microphysics scheme and the representation of coldphase processes have been shown to impact the sensitivity of convective clouds to aerosol (Heikenfeld et al., 2019; White et al., 2017; Sullivan and Voigt, 2021; Marinescu et al., 2021), while the representation of the warm-rain process and its link to aerosols have been shown to be important for ACI impacts on warm-phase clouds (Gryspeerdt et al., 2019; Sato et al., 2018; Terai et al., 2020). Archer-Nicholls et al. (2016) and Possner et al. (2016) have shown that the magnitude of ACI and ARI impacts on clouds may be sensitive to unresolved convection at 5 km resolution, potentially requiring a finer global resolution (e.g., Wedi et al., 2020). A key reason for the model and microphysics uncertainty is the lack of observational constraints for cloud microphysical processes, particularly in convective updrafts (Johnson et al., 2015; Pathak et al., 2020; Proske et al., 2023). These will be required for evaluating and developing future global kilometer-scale simulations of aerosol–climate interactions. Intensive field campaigns targeting aerosol–convection interactions, such as the Tracking Aerosol Convection interactions ExpeRiment (TRACER) and associated campaigns (e.g., Lappin et al., 2024), will provide valuable observations, and will complement previous field campaigns (e.g., GoAmazon, Martin et al., 2016; ACRIDICON-CHUVA, Wendisch et al., 2016; and CACTI, Varble et al., 2021). However, there is a lack of intensive field-campaign observations from the convective regions of Africa and Southeast Asia. Existing remote observation platforms will soon be joined by ESA’s Earth Cloud Aerosol and Radiation Explorer (EarthCARE; Illingworth et al., 2015) and NASA’s Plankton Aerosol Clouds and Ecosystems (PACE; Gorman et al., 2019) satellite. These new missions, focusing on aerosols and clouds, will be a useful addition and help continue the longterm observational record of aerosols in the Earth system. Code and data availability. The ICON model is available under an open-source (BSD-3C) license (https://www.icon-model.org, last access: May 2022) and publicly available at https: //gitlab.dkrz.de/icon/icon-model/-/releases/icon-2025.04-1-public (Gitlab, 2025). Our specific code commit is SHA 558a9611f4de09cbd8d46ff4cd2e927b243cf58f. The MACv2SP software is implemented in the ICON model source code and is publicly available in the supplementary material of Stevens et al. (2017). Full access to the simulation output data and necessary processing resources is available upon request via the DYAMOND initiative website (https://www.gewex.org/dyamond/, last access: July 2025). All scripts used to analyze the simulation output and produce the figures in the paper are available on the Zenodo repository (https://doi.org/10.5281/zenodo.11470778, Herbert, 2024). Supplement. The supplement related to this article is available online at https://doi.org/10.5194/acp-25-7789-2025-supplement. Author contributions. PS conceptualized the research. RJH, AILW, PW, DWP, and PS designed the methodology and experiments. RJH, AILW, and PW prepared and ran the simulations. RJH and PW processed and analyzed the simulation output. RJH prepared the manuscript with contributions from all co-authors. Competing interests. At least one of the (co-)authors is a member of the editorial board of Atmospheric Chemistry and Physics. The peer-review process was guided by an independent editor, and the authors also have no other competing interests to declare. Disclaimer. Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Acknowledgements. This research was supported by the European Research Council project RECAP under the European Union’s Horizon 2020 research and innovation program (grant no. 724602) and by the FORCeS project under the European Union’s Horizon 2020 research program under grant agreement no. 821205. Philip Stier additionally acknowledges funding from the European Union’s Horizon 2020 project nextGEMS under grant agreement number 101003470 and the European Union’s Horizon Europe project CleanCloud with grant agreement 101137639 and its UKRI underwrite. Andrew I. L. Williams acknowledges funding from the CIMES Postdoctoral Fellowship under award NA18OAR4320123 from the National Oceanic and Atmospheric Administration, U.S. Department of Commerce. We thank the German Climate Computing Center (DKRZ) for use of its computer facilities with which the simulations were performed. Financial support. This research has been supported by the EU H2020 European Research Council (grant nos. 724602, 821205, and 101003470), the HORIZON EUROPE European Research Council (grant no. 101137639), and the National Oceanic and Atmospheric Administration (grant no. NA18OAR4320123). Atmos. Chem. Phys., 25, 7789–7814, 2025 https://doi.org/10.5194/acp-25-7789-2025 R. J. Herbert et al.: Aerosol–cloud–radiation interactions in global kilometer-scale simulations 7807 Review statement. This paper was edited by Hailong Wang and reviewed by three anonymous referees. References Andreae, M. O., Rosenfeld, D., Artaxo, P., Costa, A. A., Frank, G. P., Longo, K. M., and Silva-Dias, M. A.: Smoking Rain Clouds over the Amazon, Science, 303, 1337–1342, https://doi.org/10.1126/science.1092779, 2004. Archer-Nicholls, S., Lowe, D., Schultz, D. M., and McFiggans, G.: Aerosol–radiation–cloud interactions in a regional coupled model: the effects of convective parameterisation and resolution, Atmos. Chem. Phys., 16, 5573–5594, https://doi.org/10.5194/acp-16-5573-2016, 2016. Atlas, R. L., Bretherton, C. S., Khairoutdinov, M. F., and Blossey, P. N.: Hallett-Mossop Rime Splintering Dims Cumulus Clouds Over the Southern Ocean: New Insight From Nudged Global Storm-Resolving Simulations, AGU Advances, 3, e2021AV000454, https://doi.org/10.1029/2021AV000454, 2022. Atlas, R. L., Bretherton, C. S., Sokol, A. B., Blossey, P. N., and Khairoutdinov, M. F.: Tropical Cirrus Are Highly Sensitive to Ice Microphysics Within a Nudged Global StormResolving Model, Geophys. Res. Lett., 51, e2023GL105868, https://doi.org/10.1029/2023GL105868, 2024. Baldauf, M., Seifert, A., Förstner, J., Majewski, D., Raschendorfer, M., and Reinhardt, T.: Operational Convective-Scale Numerical Weather Prediction with the COSMO Model: Description and Sensitivities, Mon. Weather Rev., 139, 3887–3905, https://doi.org/10.1175/MWR-D-10-05013.1, 2011. Ban, N., Caillaud, C., Coppola, E., Pichelli, E., Sobolowski, S., Adinolfi, M., Ahrens, B., Alias, A., Anders, I., Bastin, S., Beluši´ c, D., Berthou, S., Brisson, E., Cardoso, R. M., Chan, S. C., Christensen, O. B., Fernández, J., Fita, L., Frisius, T., Gašparac, G., Giorgi, F., Goergen, K., Haugen, J. E., Hodnebrog, Ø., Kartsios, S., Katragkou, E., Kendon, E. J., Keuler, K., Lavin-Gullon, A., Lenderink, G., Leutwyler, D., Lorenz, T., Maraun, D., Mercogliano, P., Milovac, J., Panitz, H.-J., Raffa, M., Remedio, A. R., Schär, C., Soares, P. M. M., Srnec, L., Steensen, B. M., Stocchi, P., Tölle, M. H., Truhetz, H., Vergara-Temprado, J., de Vries, H., Warrach-Sagi, K., Wulfmeyer, V., and Zander, M. J.: The first multi-model ensemble of regional climate simulations at kilometer-scale resolution, part I: evaluation of precipitation, Clim. Dynam., 57, 275–302, https://doi.org/10.1007/s00382021-05708-w, 2021. Barkhordarian, A., Saatchi, S. S., Behrangi, A., Loikith, P. C., and Mechoso, C. R.: A Recent Systematic Increase in Vapor Pressure Deficit over Tropical South America, Sci. Rep., 9, 15331, https://doi.org/10.1038/s41598-019-51857-8, 2019. Bellouin, N., Quaas, J., Gryspeerdt, E., Kinne, S., Stier, P., WatsonParris, D., Boucher, O., Carslaw, K. S., Christensen, M., Daniau, A.-L., Dufresne, J.-L., Feingold, G., Fiedler, S., Forster, P., Gettelman, A., Haywood, J. M., Lohmann, U., Malavelle, F., Mauritsen, T., McCoy, D. T., Myhre, G., Mülmenstädt, J., Neubauer, D., Possner, A., Rugenstein, M., Sato, Y., Schulz, M., Schwartz, S. E., Sourdeval, O., Storelvmo, T., Toll, V., Winker, D., and Stevens, B.: Bounding Global Aerosol Radiative Forcing of Climate Change, Rev. Geophys., 58, e2019RG000660, https://doi.org/10.1029/2019RG000660, 2020. Bolot, M., Harris, L. M., Cheng, K.-Y., Merlis, T. M., Blossey, P. N., Bretherton, C. S., Clark, S. K., Kaltenbaugh, A., Zhou, L., and Fueglistaler, S.: Kilometer-scale global warming simulations and active sensors reveal changes in tropical deep convection, npj Clim. Atmos. Sci., 6, 1–8, https://doi.org/10.1038/s41612-02300525-w, 2023. Boucher, O., Randall, D., Artaxo, P., Bretherton, C., Feingold, G., Forster, P., Kerminen, V.-M., Kondo, Y., Liao, H., Lohmann, U., Rasch, P., Satheesh, S., Sherwood, S., Stevens, B., and Zhang, X.-Y.: IPCC AR5 Clouds and Aerosols, in: Climate Change 2013 – The Physical Science Basis, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, ISBN 978-1-107-66182-0, 571–658, https://doi.org/10.1017/CBO9781107415324.016, 2013. Braga, R. C., Ervens, B., Rosenfeld, D., Andreae, M. O., Förster, J.-D., Fütterer, D., Hernández Pardo, L., Holanda, B. A., JurkatWitschas, T., Krüger, O. O., Lauer, O., Machado, L. A. T., Pöhlker, C., Sauer, D., Voigt, C., Walser, A., Wendisch, M., Pöschl, U., and Pöhlker, M. L.: Cloud droplet formation at the base of tropical convective clouds: closure between modeling and measurement results of ACRIDICON–CHUVA, Atmos. Chem. Phys., 21, 17513–17528, https://doi.org/10.5194/acp-21-175132021, 2021. Bukowski, J. and van den Heever, S. C.: Direct Radiative Effects in Haboobs, J. Geophys. Res.-Atmos., 126, e2021JD034814, https://doi.org/10.1029/2021JD034814, 2021. Carslaw, D. C.: On the changing seasonal cycles and trends of ozone at Mace Head, Ireland, Atmos. Chem. Phys., 5, 3441– 3450, https://doi.org/10.5194/acp-5-3441-2005, 2005. Chang, D., Cheng, Y., Reutter, P., Trentmann, J., Burrows, S. M., Spichtinger, P., Nordmann, S., Andreae, M. O., Pöschl, U., and Su, H.: Comprehensive mapping and characteristic regimes of aerosol effects on the formation and evolution of pyro-convective clouds, Atmos. Chem. Phys., 15, 10325–10348, https://doi.org/10.5194/acp-15-10325-2015, 2015. Chang, J. H.-W., Wong, Y. J., Ooi, M. C.-G., Babu, S. R., Pani, S. K., and Lin, N.-H.: Biomass burning in critical fire region over the Maritime Continent from 2012 to 2021: A review of the meteorological influence and cloudaerosol-radiation interactions, Atmos. Environ., 320, 120324, https://doi.org/10.1016/j.atmosenv.2023.120324, 2024. Che, H., Stier, P., Gordon, H., Watson-Parris, D., and Deaconu, L.: Cloud adjustments dominate the overall negative aerosol radiative effects of biomass burning aerosols in UKESM1 climate model simulations over the south-eastern Atlantic, Atmos. Chem. Phys., 21, 17–33, https://doi.org/10.5194/acp-21-172021, 2021. Cheng, K.-Y., Harris, L., Bretherton, C., Merlis, T. M., Bolot, M., Zhou, L., Kaltenbaugh, A., Clark, S., and Fueglistaler, S.: Impact of Warmer Sea Surface Temperature on the Global Pattern of Intense Convection: Insights From a Global Storm Resolving Model, Geophys. Res. Lett., 49, e2022GL099796, https://doi.org/10.1029/2022GL099796, 2022. Cleveland, W. S.: Robust Locally Weighted Regression and Smoothing Scatterplots, J. Am. Stat. Assoc., 74, 829–836, https://doi.org/10.1080/01621459.1979.10481038, 1979. Colin, M., Sherwood, S., Geoffroy, O., Bony, S., and Fuchs, D.: Identifying the Sources of Convective Memory in https://doi.org/10.5194/acp-25-7789-2025 Atmos. Chem. Phys., 25, 7789–7814, 2025 7808 R. J. Herbert et al.: Aerosol–cloud–radiation interactions in global kilometer-scale simulations Cloud-Resolving Simulations, J. Atmos. Sci., 76, 947–962, https://doi.org/10.1175/JAS-D-18-0036.1, 2019. Creese, A. and Washington, R.: A Process-Based Assessment of CMIP5 Rainfall in the Congo Basin: The September– November Rainy Season, J. Climate, 31, 7417–7439, https://doi.org/10.1175/JCLI-D-17-0818.1, 2018. Dagan, G. and Eytan, E.: The Potential of Absorbing Aerosols to Enhance Extreme Precipitation, Geophys. Res. Lett., 51, e2024GL108385, https://doi.org/10.1029/2024GL108385, 2024. Dagan, G., Stier, P., and Watson-Parris, D.: Aerosol Forcing Masks and Delays the Formation of the North Atlantic Warming Hole by Three Decades, Geophys. Res. Lett., 47, e2020GL090778, https://doi.org/10.1029/2020GL090778, 2020. Dagan, G., Stier, P., and Watson-Parris, D.: An Energetic View on the Geographical Dependence of the Fast Aerosol Radiative Effects on Precipitation, J. Geophys. Res.-Atmos., 126, e2020JD033045, https://doi.org/10.1029/2020JD033045, 2021. Dagan, G., Stier, P., Spill, G., Herbert, R., Heikenfeld, M., Van Den Heever, S. C., and Marinescu, P. J.: Boundary conditions representation can determine simulated aerosol effects on convective cloud fields, Commun. Earth Environ., 3, 71, https://doi.org/10.1038/s43247-022-00399-5, 2022. da Silveira Bueno, C., Paytan, A., de Souza, C. D., and Franco, T. T.: Global warming and coastal protected areas: A study on phytoplankton abundance and sea surface temperature in different regions of the Brazilian South Atlantic Coastal Ocean, Ecol. Evol., 14, e11724, https://doi.org/10.1002/ece3.11724, 2024. De Deckker, P.: The Indo-Pacific Warm Pool: critical to world oceanography and world climate, Geosci. Lett., 3, 20, https://doi.org/10.1186/s40562-016-0054-3, 2016. Deng, J., Zhang, Y., Qin, B., and Shi, K.: Long-term changes in surface solar radiation and their effects on air temperature in the Shanghai region, Int. J. Climatol., 35, 3385–3396, https://doi.org/10.1002/joc.4212, 2015. Deng, Q. and Fu, Z.: Comparison of methods for extracting annual cycle with changing amplitude in climate series, Clim. Dynam., 52, 5059–5070, https://doi.org/10.1007/s00382-018-44328, 2019. Deser, C., Phillips, A. S., Simpson, I. R., Rosenbloom, N., Coleman, D., Lehner, F., Pendergrass, A. G., DiNezio, P., and Stevenson, S.: Isolating the Evolving Contributions of Anthropogenic Aerosols and Greenhouse Gases: A New CESM1 Large Ensemble Community Resource, J. Climate, 33, 7835–7858, https://doi.org/10.1175/JCLI-D-20-0123.1, 2020. Dipankar, A., Stevens, B., Heinze, R., Moseley, C., Zängl, G., Giorgetta, M., and Brdar, S.: Large eddy simulation using the general circulation model ICON, J. Adv. Model. Earth Sy., 7, 963–986, https://doi.org/10.1002/2015MS000431, 2015. Dittus, A. J., Hawkins, E., Wilcox, L. J., Sutton, R. T., Smith, C. J., Andrews, M. B., and Forster, P. M.: Sensitivity of Historical Climate Simulations to Uncertain Aerosol Forcing, Geophys. Res. Lett., 47, e2019GL085806, https://doi.org/10.1029/2019GL085806, 2020. Done, J., Davis, C. A., and Weisman, M.: The next generation of NWP: explicit forecasts of convection using the weather research and forecasting (WRF) model, Atmos. Sci. Lett., 5, 110–117, https://doi.org/10.1002/asl.72, 2004. Ekman, A. M. L.: Do sophisticated parameterizations of aerosolcloud interactions in CMIP5 models improve the representation of recent observed temperature trends?, J. Geophys. Res.- Atmos., 119, 817–832, https://doi.org/10.1002/2013JD020511, 2014. Fan, J., Leung, L. R., Rosenfeld, D., Chen, Q., Li, Z., Zhang, J., and Yan, H.: Microphysical effects determine macrophysical response for aerosol impacts on deep convective clouds, P. Natl. Acad. Sci. USA, 110, E4581–E4590, https://doi.org/10.1073/pnas.1316830110, 2013. Fan, J., Rosenfeld, D., Zhang, Y., Giangrande, S. E., Li, Z., Machado, L. A., Martin, S. T., Yang, Y., Wang, J., Artaxo, P., Barbosa, H. M., Braga, R. C., Comstock, J. M., Feng, Z., Gao, W., Gomes, H. B., Mei, F., Pöhlker, C., Pöhlker, M. L., Pöschl, U., and De Souza, R. A.: Substantial convection and precipitation enhancements by ultrafine aerosol particles, Science, 359, 411–418, https://doi.org/10.1126/science.aan8461, 2018. Fiedler, S. and Putrasahan, D.: How Does the North Atlantic SST Pattern Respond to Anthropogenic Aerosols in the 1970s and 2000s?, Geophys. Res. Lett., 48, e2020GL092142, https://doi.org/10.1029/2020GL092142, 2021. Fiedler, S., Stevens, B., and Mauritsen, T.: On the sensitivity of anthropogenic aerosol forcing to model-internal variability and parameterizing a Twomey effect, J. Adv. Model. Earth Sy., 9, 1325– 1341, https://doi.org/10.1002/2017MS000932, 2017. Fiedler, S., Kinne, S., Huang, W. T. K., Räisänen, P., O’Donnell, D., Bellouin, N., Stier, P., Merikanto, J., van Noije, T., Makkonen, R., and Lohmann, U.: Anthropogenic aerosol forcing – insights from multiple estimates from aerosol-climate models with reduced complexity, Atmos. Chem. Phys., 19, 6821–6841, https://doi.org/10.5194/acp-19-6821-2019, 2019. Fiedler, S., van Noije, T., Smith, C. J., Boucher, O., Dufresne, J.-L., Kirkevåg, A., Olivié, D., Pinto, R., Reerink, T., Sima, A., and Schulz, M.: Historical Changes and Reasons for Model Differences in Anthropogenic Aerosol Forcing in CMIP6, Geophys. Res. Lett., 50, e2023GL104848, https://doi.org/10.1029/2023GL104848, 2023. Forster, P., Storelvmo, T., Armour, K., Collins, W., Dufresne, J.- L., Frame, D., Lunt, J, D., Mauritsen, T., Palmer, D, M., Watanabe, M., Wild, M., and Zhang, H.: The Earth’s Energy Budget, Climate Feedbacks, and Climate Sensitivity, in: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 923–1054, https://doi.org/10.1017/9781009157896.009, 2021. Gitlab: icon-model, Release export ICON 2025.04-1 based on tag tags/icon-2025.04-1-public, Gitlab [code], https://gitlab.dkrz.de/ icon/icon-model/-/releases/icon-2025.04-1-public (last access: May 2025), 2025. Gliß, J., Mortier, A., Schulz, M., Andrews, E., Balkanski, Y., Bauer, S. E., Benedictow, A. M. K., Bian, H., Checa-Garcia, R., Chin, M., Ginoux, P., Griesfeller, J. J., Heckel, A., Kipling, Z., Kirkevåg, A., Kokkola, H., Laj, P., Le Sager, P., Lund, M. T., Lund Myhre, C., Matsui, H., Myhre, G., Neubauer, D., van Noije, T., North, P., Olivié, D. J. L., Rémy, S., Sogacheva, L., Takemura, T., Tsigaridis, K., and Tsyro, S. G.: AeroCom phase III multi-model evaluation of the aerosol life cycle and optical properties using groundand space-based remote sensing as well as surface in situ observations, Atmos. Chem. Phys., 21, 87–128, https://doi.org/10.5194/acp-21-87-2021, 2021. Atmos. Chem. Phys., 25, 7789–7814, 2025 https://doi.org/10.5194/acp-25-7789-2025 R. J. Herbert et al.: Aerosol–cloud–radiation interactions in global kilometer-scale simulations 7809 Gordon, H., Field, P. R., Abel, S. J., Dalvi, M., Grosvenor, D. P., Hill, A. A., Johnson, B. T., Miltenberger, A. K., Yoshioka, M., and Carslaw, K. S.: Large simulated radiative effects of smoke in the south-east Atlantic, Atmos. Chem. Phys., 18, 15261–15289, https://doi.org/10.5194/acp-18-15261-2018, 2018. Gorman, E. T., Kubalak, D. A., Patel, D., Dress, A., Mott, D. B., Meister, G., and Werdell, P. J.: The NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission: an emerging era of global, hyperspectral Earth system remote sensing, in: Sensors, Systems, and Next-Generation Satellites XXIII, edited by: Neeck, S. P., Martimort, P., and Kimura, T., International Society for Optics and Photonics, SPIE, 11151, 111510G, https://doi.org/10.1117/12.2537146, 2019. Grosvenor, D. P., Sourdeval, O., Zuidema, P., Ackerman, A., Alexandrov, M. D., Bennartz, R., Boers, R., Cairns, B., Chiu, J. C., Christensen, M., Deneke, H., Diamond, M., Feingold, G., Fridlind, A., Hünerbein, A., Knist, C., Kollias, P., Marshak, A., McCoy, D., Merk, D., Painemal, D., Rausch, J., Rosenfeld, D., Russchenberg, H., Seifert, P., Sinclair, K., Stier, P., van Diedenhoven, B., Wendisch, M., Werner, F., Wood, R., Zhang, Z., and Quaas, J.: Remote Sensing of Droplet Number Concentration in Warm Clouds: A Review of the Current State of Knowledge and Perspectives, Rev. Geophys., 56, 409–453, https://doi.org/10.1029/2017RG000593, 2018. Gryspeerdt, E., Goren, T., Sourdeval, O., Quaas, J., Mülmenstädt, J., Dipu, S., Unglaub, C., Gettelman, A., and Christensen, M.: Constraining the aerosol influence on cloud liquid water path, Atmos. Chem. Phys., 19, 5331–5347, https://doi.org/10.5194/acp19-5331-2019, 2019. Gryspeerdt, E., Povey, A. C., Grainger, R. G., Hasekamp, O., Hsu, N. C., Mulcahy, J. P., Sayer, A. M., and Sorooshian, A.: Uncertainty in aerosol–cloud radiative forcing is driven by clean conditions, Atmos. Chem. Phys., 23, 4115–4122, https://doi.org/10.5194/acp-23-4115-2023, 2023. Hamilton, D. S., Hantson, S., Scott, C. E., Kaplan, J. O., Pringle, K. J., Nieradzik, L. P., Rap, A., Folberth, G. A., Spracklen, D. V., and Carslaw, K. S.: Reassessment of pre-industrial fire emissions strongly affects anthropogenic aerosol forcing, Nat. Commun., 9, 3182, https://doi.org/10.1038/s41467-018-05592-9, 2018. He, R., Zhang, L., and Chew, A. W. Z.: Modeling and predicting rainfall time series using seasonal-trend decomposition and machine learning, Knowl.-Based Syst., 251, 109125, https://doi.org/10.1016/j.knosys.2022.109125, 2022. Heever, S. C. v. d., Carrió, G. G., Cotton, W. R., DeMott, P. J., and Prenni, A. J.: Impacts of Nucleating Aerosol on Florida Storms. Part I: Mesoscale Simulations, J. Atmos. Sci., 63, 1752–1775, https://doi.org/10.1175/JAS3713.1, 2006. Heikenfeld, M., White, B., Labbouz, L., and Stier, P.: Aerosol effects on deep convection: the propagation of aerosol perturbations through convective cloud microphysics, Atmos. Chem. Phys., 19, 2601–2627, https://doi.org/10.5194/acp-192601-2019, 2019. Herbert, R. and Stier, P.: Satellite observations of smoke–cloud– radiation interactions over the Amazon rainforest, Atmos. Chem. Phys., 23, 4595–4616, https://doi.org/10.5194/acp-234595-2023, 2023. Herbert, R., Stier, P., and Dagan, G.: Isolating Large-Scale Smoke Impacts on Cloud and Precipitation Processes Over the Amazon With Convection Permitting Resolution, J. Geophys. Res.-Atmos., 126, e2021JD034615, https://doi.org/10.1029/2021JD034615, 2021a. Herbert, R., Wilcox, L. J., Joshi, M., Highwood, E., and Frame, D.: Nonlinear response of Asian summer monsoon precipitation to emission reductions in South and East Asia, Environ. Res. Lett., 17, 014005, https://doi.org/10.1088/1748-9326/ac3b19, 2021b. Herbert, R. J.: Dataset for manuscript “Isolating aerosol-climate interactions in global kilometre-scale simulations”, Zenodo [data set], https://doi.org/10.5281/zenodo.11470778, 2024. Herbert, R. J., Bellouin, N., Highwood, E. J., and Hill, A. A.: Diurnal cycle of the semi-direct effect from a persistent absorbing aerosol layer over marine stratocumulus in large-eddy simulations, Atmos. Chem. Phys., 20, 1317–1340, https://doi.org/10.5194/acp-20-1317-2020, 2020. Hodzic, A. and Duvel, J. P.: Impact of Biomass Burning Aerosols on the Diurnal Cycle of Convective Clouds and Precipitation Over a Tropical Island, J. Geophys. Res.-Atmos., 123, 1017–1036, https://doi.org/10.1002/2017JD027521, 2018. Hohenegger, C., Kornblueh, L., Klocke, D., Becker, T., Cioni, G., Engels, J. F., Schulzweida, U., and Stevens, B.: Climate Statistics in Global Simulations of the Atmosphere, from 80 to 2.5 km Grid Spacing, J. Meteorol. Soc. Jpn. Ser. II, 98, 73–91, https://doi.org/10.2151/jmsj.2020-005, 2020. Hohenegger, C., Korn, P., Linardakis, L., Redler, R., Schnur, R., Adamidis, P., Bao, J., Bastin, S., Behravesh, M., Bergemann, M., Biercamp, J., Bockelmann, H., Brokopf, R., Brüggemann, N., Casaroli, L., Chegini, F., Datseris, G., Esch, M., George, G., Giorgetta, M., Gutjahr, O., Haak, H., Hanke, M., Ilyina, T., Jahns, T., Jungclaus, J., Kern, M., Klocke, D., Kluft, L., Kölling, T., Kornblueh, L., Kosukhin, S., Kroll, C., Lee, J., Mauritsen, T., Mehlmann, C., Mieslinger, T., Naumann, A. K., Paccini, L., Peinado, A., Praturi, D. S., Putrasahan, D., Rast, S., Riddick, T., Roeber, N., Schmidt, H., Schulzweida, U., Schütte, F., Segura, H., Shevchenko, R., Singh, V., Specht, M., Stephan, C. C., von Storch, J.-S., Vogel, R., Wengel, C., Winkler, M., Ziemen, F., Marotzke, J., and Stevens, B.: ICON-Sapphire: simulating the components of the Earth system and their interactions at kilometer and subkilometer scales, Geosci. Model Dev., 16, 779–811, https://doi.org/10.5194/gmd-16-779-2023, 2023. Hudson, J. G. and Yum, S. S.: Maritime–Continental Drizzle Contrasts in Small Cumuli, J. Atmos. Sci., 58, 915–926, https://doi.org/10.1175/15200469(2001)058<0915:MCDCIS>2.0.CO;2, 2001. Igel, A. L. and van den Heever, S. C.: Invigoration or Enervation of Convective Clouds by Aerosols?, Geophys. Res. Lett., 48, e2021GL093804, https://doi.org/10.1029/2021GL093804, 2021. Illingworth, A. J., Barker, H. W., Beljaars, A., Ceccaldi, M., Chepfer, H., Clerbaux, N., Cole, J., Delanoë, J., Domenech, C., Donovan, D. P., Fukuda, S., Hirakata, M., Hogan, R. J., Huenerbein, A., Kollias, P., Kubota, T., Nakajima, T., Nakajima, T. Y., Nishizawa, T., Ohno, Y., Okamoto, H., Oki, R., Sato, K., Satoh, M., Shephard, M. W., Velázquez-Blázquez, A., Wandinger, U., Wehr, T., and van Zadelhoff, G.-J.: The EarthCARE Satellite: The Next Step Forward in Global Measurements of Clouds, Aerosols, Precipitation, and Radiation, Bull. Am. Meteorol. Soc., 96, 1311–1332, https://doi.org/10.1175/BAMS-D12-00227.1, 2015. Jaber, S. M. and Abu-Allaban, M. M.: MODIS-based land surface temperature for climate variability and change research: the tale https://doi.org/10.5194/acp-25-7789-2025 Atmos. Chem. Phys., 25, 7789–7814, 2025 7810 R. J. Herbert et al.: Aerosol–cloud–radiation interactions in global kilometer-scale simulations of a typical semi-arid to arid environment, Eur. J. Remote Sens., 53, 81–90, https://doi.org/10.1080/22797254.2020.1735264, 2020. Jiang, J. H., Su, H., Huang, L., Wang, Y., Massie, S., Zhao, B., Omar, A., and Wang, Z.: Contrasting effects on deep convective clouds by different types of aerosols, Nat. Commun., 9, 3874, https://doi.org/10.1038/s41467-018-06280-4, 2018. Johnson, B. T., Shine, K. P., and Forster, P. M.: The semidirect aerosol effect: Impact of absorbing aerosols on marine stratocumulus, Q. J. Roy. Meteor. Soc., 130, 1407–1422, https://doi.org/10.1256/qj.03.61, 2004. Johnson, J. S., Cui, Z., Lee, L. A., Gosling, J. P., Blyth, A. M., and Carslaw, K. S.: Evaluating uncertainty in convective cloud microphysics using statistical emulation, J. Adv. Model. Earth Sy., 7, 162–187, https://doi.org/10.1002/2014MS000383, 2015. Kendon, E. J., Stratton, R. A., Tucker, S., Marsham, J. H., Berthou, S., Rowell, D. P., and Senior, C. A.: Enhanced future changes in wet and dry extremes over Africa at convection-permitting scale, Nat. Commun., 10, 1794, https://doi.org/10.1038/s41467019-09776-9, 2019. Keshtgar, B., Voigt, A., Hoose, C., Riemer, M., and Mayer, B.: Cloud-radiative impact on the dynamics and predictability of an idealized extratropical cyclone, Weather Clim. Dynam., 4, 115– 132, https://doi.org/10.5194/wcd-4-115-2023, 2023. Khain, A. P., BenMoshe, N., and Pokrovsky, A.: Factors Determining the Impact of Aerosols on Surface Precipitation from Clouds: An Attempt at Classification, J. Atmos. Sci., 65, 1721–1748, https://doi.org/10.1175/2007JAS2515.1, 2008. Kinne, S.: Aerosol radiative effects with MACv2, Atmos. Chem. Phys., 19, 10919–10959, https://doi.org/10.5194/acp-19-109192019, 2019. Koch, D. and Del Genio, A. D.: Black carbon semi-direct effects on cloud cover: review and synthesis, Atmos. Chem. Phys., 10, 7685–7696, https://doi.org/10.5194/acp-10-7685-2010, 2010. Koren, I., Kaufman, Y. J., Remer, L. A., and Martins, J. V.: Measurement of the Effect of Amazon Smoke on Inhibition of Cloud Formation, Science, 303, 1342–1345, https://doi.org/10.1126/science.1089424, 2004. Koren, I., Vanderlei Martins, J., Remer, L. A., and Afargan, H.: Smoke invigoration versus inhibition of clouds over the amazon, Science, 321, 946–949, https://doi.org/10.1126/science.1159185, 2008. Langton, T., Stier, P., Watson-Parris, D., and Mulcahy, J. P.: Decomposing Effective Radiative Forcing Due to Aerosol Cloud Interactions by Global Cloud Regimes, Geophys. Res. Lett., 48, e2021GL093833, https://doi.org/10.1029/2021GL093833, 2021. Lappin, F., de Boer, G., Klein, P., Hamilton, J., Spencer, M., Calmer, R., Segales, A. R., Rhodes, M., Bell, T. M., Buchli, J., Britt, K., Asher, E., Medina, I., Butterworth, B., Otterstatter, L., Ritsch, M., Puxley, B., Miller, A., Jordan, A., GomezFaulk, C., Smith, E., Borenstein, S., Thornberry, T., Argrow, B., and Pillar-Little, E.: Data collected using small uncrewed aircraft systems during the TRacking Aerosol Convection interactions ExpeRiment (TRACER), Earth Syst. Sci. Data, 16, 2525–2541, https://doi.org/10.5194/essd-16-2525-2024, 2024. Lauk, C. and Erb, K.-H.: Biomass consumed in anthropogenic vegetation fires: Global patterns and processes, Ecol. Econ., 69, 301– 309, https://doi.org/10.1016/j.ecolecon.2009.07.003, 2009. Lebo, Z.: A numerical investigation of the potential effects of aerosol-induced warming and updraft width and slope on updraft intensity in deep convective clouds, J. Atmos. Sci., 75, 535–554, https://doi.org/10.1175/JAS-D-16-0368.1, 2018. Lebo, Z. J. and Seinfeld, J. H.: Theoretical basis for convective invigoration due to increased aerosol concentration, Atmos. Chem. Phys., 11, 5407–5429, https://doi.org/10.5194/acp11-5407-2011, 2011. Lee, H.-H. and Wang, C.: The impacts of biomass burning activities on convective systems over the Maritime Continent, Atmos. Chem. Phys., 20, 2533–2548, https://doi.org/10.5194/acp20-2533-2020, 2020. Lee, S. S., Feingold, G., McComiskey, A., Yamaguchi, T., Koren, I., Vanderlei Martins, J., and Yu, H.: Effect of gradients in biomass burning aerosol on shallow cumulus convective circulations, J. Geophys. Res.-Atmos., 119, 9948–9964, https://doi.org/10.1002/2014JD021819, 2014. Liu, L., Cheng, Y., Wang, S., Wei, C., Pöhlker, M. L., Pöhlker, C., Artaxo, P., Shrivastava, M., Andreae, M. O., Pöschl, U., and Su, H.: Impact of biomass burning aerosols on radiation, clouds, and precipitation over the Amazon: relative importance of aerosol– cloud and aerosol–radiation interactions, Atmos. Chem. Phys., 20, 13283–13301, https://doi.org/10.5194/acp-20-13283-2020, 2020. Liu, L., Lin, C., Cui, C., Wang, C., Yang, H., and Li, S.: Contrasting aerosol effects on shallow and deep convections during the Mei-yu season in China, Atmos. Res., 309, 107599, https://doi.org/10.1016/j.atmosres.2024.107599, 2024. Liu, X. and Zhang, Q.: Combining Seasonal and Trend Decomposition Using LOESS With a Gated Recurrent Unit for Climate Time Series Forecasting, IEEE Access, 12, 85275–85290, https://doi.org/10.1109/ACCESS.2024.3415349, 2024. Liu, Z., Yim, S. H. L., Wang, C., and Lau, N. C.: The Impact of the Aerosol Direct Radiative Forcing on Deep Convection and Air Quality in the Pearl River Delta Region, Geophys. Res. Lett., 45, 4410–4418, https://doi.org/10.1029/2018GL077517, 2018. Lorenz, E. N.: Deterministic Nonperiodic Flow, J. Atmos. Sci., 20, 130–141, https://doi.org/10.1175/15200469(1963)020<0130:DNF>2.0.CO;2, 1963. Lu, Z., Liu, X., Zhang, Z., Zhao, C., Meyer, K., Rajapakshe, C., Wu, C., Yang, Z., and Penner, J. E.: Biomass smoke from southern Africa can significantly enhance the brightness of stratocumulus over the southeastern Atlantic Ocean, P. Natl. Acad. Sci. USA, 115, 2924–2929, https://doi.org/10.1073/pnas.1713703115, 2018. Machado, L. A. T., Calheiros, A. J. P., Biscaro, T., Giangrande, S., Silva Dias, M. A. F., Cecchini, M. A., Albrecht, R., Andreae, M. O., Araujo, W. F., Artaxo, P., Borrmann, S., Braga, R., Burleyson, C., Eichholz, C. W., Fan, J., Feng, Z., Fisch, G. F., Jensen, M. P., Martin, S. T., Pöschl, U., Pöhlker, C., Pöhlker, M. L., Ribaud, J.-F., Rosenfeld, D., Saraiva, J. M. B., Schumacher, C., Thalman, R., Walter, D., and Wendisch, M.: Overview: Precipitation characteristics and sensitivities to environmental conditions during GoAmazon2014/5 and ACRIDICON-CHUVA, Atmos. Chem. Phys., 18, 6461–6482, https://doi.org/10.5194/acp18-6461-2018, 2018. Mann, G. W., Carslaw, K. S., Reddington, C. L., Pringle, K. J., Schulz, M., Asmi, A., Spracklen, D. V., Ridley, D. A., Woodhouse, M. T., Lee, L. A., Zhang, K., Ghan, S. J., Easter, R. C., Atmos. Chem. Phys., 25, 7789–7814, 2025 https://doi.org/10.5194/acp-25-7789-2025 R. J. Herbert et al.: Aerosol–cloud–radiation interactions in global kilometer-scale simulations 7811 Liu, X., Stier, P., Lee, Y. H., Adams, P. J., Tost, H., Lelieveld, J., Bauer, S. E., Tsigaridis, K., van Noije, T. P. C., Strunk, A., Vignati, E., Bellouin, N., Dalvi, M., Johnson, C. E., Bergman, T., Kokkola, H., von Salzen, K., Yu, F., Luo, G., Petzold, A., Heintzenberg, J., Clarke, A., Ogren, J. A., Gras, J., Baltensperger, U., Kaminski, U., Jennings, S. G., O’Dowd, C. D., Harrison, R. M., Beddows, D. C. S., Kulmala, M., Viisanen, Y., Ulevicius, V., Mihalopoulos, N., Zdimal, V., Fiebig, M., Hansson, H.-C., Swietlicki, E., and Henzing, J. S.: Intercomparison and evaluation of global aerosol microphysical properties among AeroCom models of a range of complexity, Atmos. Chem. Phys., 14, 4679–4713, https://doi.org/10.5194/acp-14-4679-2014, 2014. Mapes, B. and Neale, R.: Parameterizing Convective Organization to Escape the Entrainment Dilemma, J. Adv. Model. Earth Sy., 3, M06004, https://doi.org/10.1029/2011MS000042, 2011. Marinescu, P. J., Heever, S. C. v. d., Heikenfeld, M., Barrett, A. I., Barthlott, C., Hoose, C., Fan, J., Fridlind, A. M., Matsui, T., Miltenberger, A. K., Stier, P., Vie, B., White, B. A., and Zhang, Y.: Impacts of Varying Concentrations of Cloud Condensation Nuclei on Deep Convective Cloud Updrafts – A Multimodel Assessment, J. Atmos. Sci., 78, 1147–1172, https://doi.org/10.1175/JAS-D-20-0200.1, 2021. Martin, S. T., Artaxo, P., Machado, L. A. T., Manzi, A. O., Souza, R. A. F., Schumacher, C., Wang, J., Andreae, M. O., Barbosa, H. M. J., Fan, J., Fisch, G., Goldstein, A. H., Guenther, A., Jimenez, J. L., Pöschl, U., Silva Dias, M. A., Smith, J. N., and Wendisch, M.: Introduction: Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5), Atmos. Chem. Phys., 16, 4785– 4797, https://doi.org/10.5194/acp-16-4785-2016, 2016. Martins, J. A., Silva Dias, M. A. F., and Gonçalves, F. L. T.: Impact of biomass burning aerosols on precipitation in the Amazon: A modeling case study, J. Geophys. Res., 114, D02207, https://doi.org/10.1029/2007jd009587, 2009. McCoy, D. T., Bender, F. A.-M., Grosvenor, D. P., Mohrmann, J. K., Hartmann, D. L., Wood, R., and Field, P. R.: Predicting decadal trends in cloud droplet number concentration using reanalysis and satellite data, Atmos. Chem. Phys., 18, 2035–2047, https://doi.org/10.5194/acp-18-2035-2018, 2018. Michibata, T., Suzuki, K., Sato, Y., and Takemura, T.: The source of discrepancies in aerosol–cloud–precipitation interactions between GCM and A-Train retrievals, Atmos. Chem. Phys., 16, 15413–15424, https://doi.org/10.5194/acp-16-154132016, 2016. Miles, N. L., Verlinde, J., and Clothiaux, E. E.: Cloud Droplet Size Distributions in Low-Level Stratiform Clouds, J. Atmos. Sci., 57, 295–311, https://doi.org/10.1175/15200469(2000)057<0295:CDSDIL>2.0.CO;2, 2000. Moradi, M.: Wavelet transform approach for denoising and decomposition of satellite-derived ocean color time-series: Selection of optimal mother wavelet, Adv. Space Res., 69, 2724–2744, https://doi.org/10.1016/j.asr.2022.01.023, 2022. Myhre, G., Samset, B. H., Schulz, M., Balkanski, Y., Bauer, S., Berntsen, T. K., Bian, H., Bellouin, N., Chin, M., Diehl, T., Easter, R. C., Feichter, J., Ghan, S. J., Hauglustaine, D., Iversen, T., Kinne, S., Kirkevåg, A., Lamarque, J.-F., Lin, G., Liu, X., Lund, M. T., Luo, G., Ma, X., van Noije, T., Penner, J. E., Rasch, P. J., Ruiz, A., Seland, Ø., Skeie, R. B., Stier, P., Takemura, T., Tsigaridis, K., Wang, P., Wang, Z., Xu, L., Yu, H., Yu, F., Yoon, J.-H., Zhang, K., Zhang, H., and Zhou, C.: Radiative forcing of the direct aerosol effect from AeroCom Phase II simulations, Atmos. Chem. Phys., 13, 1853–1877, https://doi.org/10.5194/acp13-1853-2013, 2013. Myhre, G., Kramer, R. J., Smith, C. J., Hodnebrog, Ø., Forster, P., Soden, B. J., Samset, B. H., Stjern, C. W., Andrews, T., Boucher, O., Faluvegi, G., Fläschner, D., Kasoar, M., Kirkevåg, A., Lamarque, J., Olivié, D., Richardson, T., Shindell, D., Stier, P., Takemura, T., Voulgarakis, A., and Watson-Parris, D.: Quantifying the Importance of Rapid Adjustments for Global Precipitation Changes, Geophys. Res. Lett., 45, 11399–11405, https://doi.org/10.1029/2018GL079474, 2018. O’Connor, F. M., Abraham, N. L., Dalvi, M., Folberth, G. A., Griffiths, P. T., Hardacre, C., Johnson, B. T., Kahana, R., Keeble, J., Kim, B., Morgenstern, O., Mulcahy, J. P., Richardson, M., Robertson, E., Seo, J., Shim, S., Teixeira, J. C., Turnock, S. T., Williams, J., Wiltshire, A. J., Woodward, S., and Zeng, G.: Assessment of pre-industrial to present-day anthropogenic climate forcing in UKESM1, Atmos. Chem. Phys., 21, 1211–1243, https://doi.org/10.5194/acp-21-1211-2021, 2021. Palmer, T. and Stevens, B.: The scientific challenge of understanding and estimating climate change, P. Natl. Acad. Sci. USA, 116, 24390–24395, https://doi.org/10.1073/pnas.1906691116, 2019. Papacharalampous, G., Tyralis, H., and Koutsoyiannis, D.: Predictability of monthly temperature and precipitation using automatic time series forecasting methods, Acta Geophys., 66, 807– 831, https://doi.org/10.1007/s11600-018-0120-7, 2018. Park, J. M. and van den Heever, S. C.: Weakening of tropical sea breeze convective systems through interactions of aerosol, radiation, and soil moisture, Atmos. Chem. Phys., 22, 10527–10549, https://doi.org/10.5194/acp-22-10527-2022, 2022. Pathak, R., Sahany, S., and Mishra, S. K.: Uncertainty quantification based cloud parameterization sensitivity analysis in the NCAR community atmosphere model, Sci. Rep., 10, 17499, https://doi.org/10.1038/s41598-020-74441-x, 2020. Pincus, R., Mlawer, E. J., and Delamere, J. S.: Balancing Accuracy, Efficiency, and Flexibility in Radiation Calculations for Dynamical Models, J. Adv. Model. Earth Sy., 11, 3074–3089, https://doi.org/10.1029/2019MS001621, 2019. Platnick, S., Hubanks, P., Meyer, K., and King, M. D.: MODIS Atmosphere L3 Monthly Product (08_L3), NASA MODIS Adaptive processing system, Goddard Space Flight Center, USA [data set], https://modis.gsfc.nasa.gov/data/dataprod/mod08.php (last access: September 2022), 2015. Possner, A., Zubler, E., Lohmann, U., and Schär, C.: The resolution dependence of cloud effects and ship-induced aerosol-cloud interactions in marine stratocumulus, J. Geophys. Res.-Atmos., 121, 4810–4829, https://doi.org/10.1002/2015JD024685, 2016. Prein, A. F., Gobiet, A., Suklitsch, M., Truhetz, H., Awan, N. K., Keuler, K., and Georgievski, G.: Added value of convection permitting seasonal simulations, Clim. Dynam., 41, 2655–2677, https://doi.org/10.1007/s00382-013-1744-6, 2013. Pringle, K. J., Carslaw, K. S., Spracklen, D. V., Mann, G. M., and Chipperfield, M. P.: The relationship between aerosol and cloud drop number concentrations in a global aerosol microphysics model, Atmos. Chem. Phys., 9, 4131–4144, https://doi.org/10.5194/acp-9-4131-2009, 2009. Proske, U., Ferrachat, S., Klampt, S., Abeling, M., and Lohmann, U.: Addressing Complexity in Global Aerosol Climate Model Cloud Microphysics, J. Adv. Model. Earth Sy., https://doi.org/10.5194/acp-25-7789-2025 Atmos. Chem. Phys., 25, 7789–7814, 2025 7812 R. J. Herbert et al.: Aerosol–cloud–radiation interactions in global kilometer-scale simulations 15, e2022MS003571, https://doi.org/10.1029/2022MS003571, 2023. Quan, J., Zhan, W., Chen, Y., Wang, M., and Wang, J.: Time series decomposition of remotely sensed land surface temperature and investigation of trends and seasonal variations in surface urban heat islands, J. Geophys. Res.-Atmos., 121, 2638–2657, https://doi.org/10.1002/2015JD024354, 2016. Rabbi, M. F. and Kovács, S.: Quantifying global warming potential variations from greenhouse gas emission sources in forest ecosystems, Carbon Res., 3, 70, https://doi.org/10.1007/s44246024-00156-7, 2024. Randles, C. A., Kinne, S., Myhre, G., Schulz, M., Stier, P., Fischer, J., Doppler, L., Highwood, E., Ryder, C., Harris, B., Huttunen, J., Ma, Y., Pinker, R. T., Mayer, B., Neubauer, D., Hitzenberger, R., Oreopoulos, L., Lee, D., Pitari, G., Di Genova, G., Quaas, J., Rose, F. G., Kato, S., Rumbold, S. T., Vardavas, I., Hatzianastassiou, N., Matsoukas, C., Yu, H., Zhang, F., Zhang, H., and Lu, P.: Intercomparison of shortwave radiative transfer schemes in global aerosol modeling: results from the AeroCom Radiative Transfer Experiment, Atmos. Chem. Phys., 13, 2347–2379, https://doi.org/10.5194/acp-13-2347-2013, 2013. Recchia, L. G. and Lucarini, V.: Modelling the effect of aerosol and greenhouse gas forcing on the South Asian and East Asian monsoons with an intermediate-complexity climate model, Earth Syst. Dynam., 14, 697–722, https://doi.org/10.5194/esd-14-6972023, 2023. Regayre, L. A., Johnson, J. S., Yoshioka, M., Pringle, K. J., Sexton, D. M. H., Booth, B. B. B., Lee, L. A., Bellouin, N., and Carslaw, K. S.: Aerosol and physical atmosphere model parameters are both important sources of uncertainty in aerosol ERF, Atmos. Chem. Phys., 18, 9975–10006, https://doi.org/10.5194/acp18-9975-2018, 2018. Reick, C. H., Raddatz, T., Brovkin, V., and Gayler, V.: Representation of natural and anthropogenic land cover change in MPI-ESM, J. Adv. Model. Earth Sy., 5, 459–482, https://doi.org/10.1002/jame.20022, 2013. Sakaeda, N., Wood, R., and Rasch, P. J.: Direct and semidirect aerosol effects of southern African biomass burning aerosol, J. Geophys. Res.-Atmos., 116, D12205, https://doi.org/10.1029/2010JD015540, 2011. Sand, M., Samset, B. H., Myhre, G., Gliß, J., Bauer, S. E., Bian, H., Chin, M., Checa-Garcia, R., Ginoux, P., Kipling, Z., Kirkevåg, A., Kokkola, H., Le Sager, P., Lund, M. T., Matsui, H., van Noije, T., Olivié, D. J. L., Remy, S., Schulz, M., Stier, P., Stjern, C. W., Takemura, T., Tsigaridis, K., Tsyro, S. G., and Watson-Parris, D.: Aerosol absorption in global models from AeroCom phase III, Atmos. Chem. Phys., 21, 15929–15947, https://doi.org/10.5194/acp-21-15929-2021, 2021. Sato, Y., Goto, D., Michibata, T., Suzuki, K., Takemura, T., Tomita, H., and Nakajima, T.: Aerosol effects on cloud water amounts were successfully simulated by a global cloud-system resolving model, Nat. Commun., 9, 985, https://doi.org/10.1038/s41467018-03379-6, 2018. Savarin, A. and Chen, S. S.: Pathways to Better Prediction of the MJO: 1. Effects of Model Resolution and Moist Physics on Atmospheric Boundary Layer and Precipitation, J. Adv. Model. Earth Sy., 14, e2021MS002928, https://doi.org/10.1029/2021MS002928, 2022. Schulz, M., Textor, C., Kinne, S., Balkanski, Y., Bauer, S., Berntsen, T., Berglen, T., Boucher, O., Dentener, F., Guibert, S., Isaksen, I. S. A., Iversen, T., Koch, D., Kirkevåg, A., Liu, X., Montanaro, V., Myhre, G., Penner, J. E., Pitari, G., Reddy, S., Seland, Ø., Stier, P., and Takemura, T.: Radiative forcing by aerosols as derived from the AeroCom present-day and pre-industrial simulations, Atmos. Chem. Phys., 6, 5225–5246, https://doi.org/10.5194/acp6-5225-2006, 2006. Segura, H., Hohenegger, C., Wengel, C., and Stevens, B.: Learning by Doing: Seasonal and Diurnal Features of Tropical Precipitation in a Global-Coupled StormResolving Model, Geophys. Res. Lett., 49, e2022GL101796, https://doi.org/10.1029/2022GL101796, 2022. Seifert, A. and Beheng, K. D.: A two-moment cloud microphysics parameterization for mixed-phase clouds. Part 1: Model description, Meteorol. Atmos. Phys., 92, 45–66, https://doi.org/10.1007/s00703-005-0112-4, 2006. Shamekh, S., Lamb, K. D., Huang, Y., and Gentine, P.: Implicit learning of convective organization explains precipitation stochasticity, P. Natl. Acad. Sci. USA, 120, e2216158120, https://doi.org/10.1073/pnas.2216158120, 2023. Sheffield, A. M., Saleeby, S. M., and van den Heever, S. C.: Aerosol-induced mechanisms for cumulus congestus growth, J. Geophys. Res.-Atmos., 120, 8941–8952, https://doi.org/10.1002/2015JD023743, 2015. Smagorinsky, J.: General Circulation Experiments With The Primitive Equations: I. The Basic Experiment, Mon. Weather Rev., 91, 99–164, https://doi.org/10.1175/15200493(1963)091<0099:GCEWTP>2.3.CO;2, 1963. Squires, P.: The Microstructure and Colloidal Stability of Warm Clouds, Tellus, 10, 256–261, https://doi.org/10.1111/j.21533490.1958.tb02011.x, 1958. Stevens, B., Giorgetta, M., Esch, M., Mauritsen, T., Crueger, T., Rast, S., Salzmann, M., Schmidt, H., Bader, J., Block, K., Brokopf, R., Fast, I., Kinne, S., Kornblueh, L., Lohmann, U., Pincus, R., Reichler, T., and Roeckner, E.: Atmospheric component of the MPI-M Earth System Model: ECHAM6, J. Adv. Model. Earth Sy., 5, 146–172, https://doi.org/10.1002/jame.20015, 2013. Stevens, B., Fiedler, S., Kinne, S., Peters, K., Rast, S., Müsse, J., Smith, S. J., and Mauritsen, T.: MACv2-SP: a parameterization of anthropogenic aerosol optical properties and an associated Twomey effect for use in CMIP6, Geosci. Model Dev., 10, 433– 452, https://doi.org/10.5194/gmd-10-433-2017, 2017. Stevens, B., Satoh, M., Auger, L., Biercamp, J., Bretherton, C. S., Chen, X., Düben, P., Judt, F., Khairoutdinov, M., Klocke, D., Kodama, C., Kornblueh, L., Lin, S.-J., Neumann, P., Putman, W. M., Röber, N., Shibuya, R., Vanniere, B., Vidale, P. L., Wedi, N., and Zhou, L.: DYAMOND: the DYnamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains, Progress in Earth and Planetary Science, 6, 61, https://doi.org/10.1186/s40645-019-0304-z, 2019. Stier, P., Schutgens, N. A. J., Bellouin, N., Bian, H., Boucher, O., Chin, M., Ghan, S., Huneeus, N., Kinne, S., Lin, G., Ma, X., Myhre, G., Penner, J. E., Randles, C. A., Samset, B., Schulz, M., Takemura, T., Yu, F., Yu, H., and Zhou, C.: Host model uncertainties in aerosol radiative forcing estimates: results from the AeroCom Prescribed intercomparison study, Atmos. Chem. Phys., 13, 3245–3270, https://doi.org/10.5194/acp-13-3245-2013, 2013. Atmos. Chem. Phys., 25, 7789–7814, 2025 https://doi.org/10.5194/acp-25-7789-2025 R. J. Herbert et al.: Aerosol–cloud–radiation interactions in global kilometer-scale simulations 7813 Stjern, C. W., Joshi, M., Wilcox, L. J., Gollop, A., and Samset, B. H.: Systematic Regional Aerosol Perturbations (SyRAP) in Asia Using the Intermediate-Resolution Global Climate Model FORTE2, J. Adv. Model. Earth Sy., 16, e2023MS004171, https://doi.org/10.1029/2023MS004171, 2024. Storer, R. L., Heever, S. C. v. d., and Stephens, G. L.: Modeling Aerosol Impacts on Convective Storms in Different Environments, J. Atmos. Sci., 67, 3904–3915, https://doi.org/10.1175/2010JAS3363.1, 2010. Sullivan, S. C. and Voigt, A.: Ice microphysical processes exert a strong control on the simulated radiative energy budget in the tropics, Commun. Earth Environ., 2, 1–8, https://doi.org/10.1038/s43247-021-00206-7, 2021. Takeishi, A. and Wang, C.: Radiative and microphysical responses of clouds to an anomalous increase in fire particles over the Maritime Continent in 2015, Atmos. Chem. Phys., 22, 4129–4147, https://doi.org/10.5194/acp-22-4129-2022, 2022. Tan, Z., Kaul, C. M., Pressel, K. G., Cohen, Y., Schneider, T., and Teixeira, J.: An Extended Eddy-Diffusivity Mass-Flux Scheme for Unified Representation of Subgrid-Scale Turbulence and Convection, J. Adv. Model. Earth Sy., 10, 770–800, https://doi.org/10.1002/2017MS001162, 2018. Tang, S., Wang, H., Li, X.-Y., Chen, J., Sorooshian, A., Zeng, X., Crosbie, E., Thornhill, K. L., Ziemba, L. D., and Voigt, C.: Understanding aerosol–cloud interactions using a singlecolumn model for a cold-air outbreak case during the ACTIVATE campaign, Atmos. Chem. Phys., 24, 10073–10092, https://doi.org/10.5194/acp-24-10073-2024, 2024. Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An Overview of CMIP5 and the Experiment Design, B. Am. Meteorol. Soc., 93, 485–498, https://doi.org/10.1175/BAMS-D-11-00094.1, 2012. Ten Hoeve, J. E., Remer, L. A., and Jacobson, M. Z.: Microphysical and radiative effects of aerosols on warm clouds during the Amazon biomass burning season as observed by MODIS: impacts of water vapor and land cover, Atmos. Chem. Phys., 11, 3021–3036, https://doi.org/10.5194/acp-11-3021-2011, 2011. Terai, C. R., Pritchard, M. S., Blossey, P., and Bretherton, C. S.: The Impact of Resolving Subkilometer Processes on AerosolCloud Interactions of Low-Level Clouds in Global Model Simulations, J. Adv. Model. Earth Sy., 12, e2020MS002274, https://doi.org/10.1029/2020MS002274, 2020. Thornhill, G. D., Ryder, C. L., Highwood, E. J., Shaffrey, L. C., and Johnson, B. T.: The effect of South American biomass burning aerosol emissions on the regional climate, Atmos. Chem. Phys., 18, 5321–5342, https://doi.org/10.5194/acp-185321-2018, 2018. Torres, O. and Ahn, C.: Local and Regional Diurnal Variability of Aerosol Properties Retrieved by DSCOVR/EPIC UV Algorithm, J. Geophys. Res.-Atmos., 129, e2023JD039908, https://doi.org/10.1029/2023JD039908, 2024. van der Werf, G. R., Randerson, J. T., Giglio, L., van Leeuwen, T. T., Chen, Y., Rogers, B. M., Mu, M., van Marle, M. J. E., Morton, D. C., Collatz, G. J., Yokelson, R. J., and Kasibhatla, P. S.: Global fire emissions estimates during 1997–2016, Earth Syst. Sci. Data, 9, 697–720, https://doi.org/10.5194/essd-9-697-2017, 2017. Varble, A. C., Nesbitt, S. W., Salio, P., Hardin, J. C., Bharadwaj, N., Borque, P., DeMott, P. J., Feng, Z., Hill, T. C. J., Marquis, J. N., Matthews, A., Mei, F., Öktem, R., Castro, V., Goldberger, L., Hunzinger, A., Barry, K. R., Kreidenweis, S. M., McFarquhar, G. M., McMurdie, L. A., Pekour, M., Powers, H., Romps, D. M., Saulo, C., Schmid, B., Tomlinson, J. M., van den Heever, S. C., Zelenyuk, A., Zhang, Z., and Zipser, E. J.: Utilizing a StormGenerating Hotspot to Study Convective Cloud Transitions: The CACTI Experiment, B. Am. Meteorol. Soc., 102, E1597–E1620, https://doi.org/10.1175/BAMS-D-20-0030.1, 2021. Verbesselt, J., Hyndman, R., Newnham, G., and Culvenor, D.: Detecting trend and seasonal changes in satellite image time series, Remote Sens. Environ., 114, 106–115, https://doi.org/10.1016/j.rse.2009.08.014, 2010. Vogel, A., Alessa, G., Scheele, R., Weber, L., Dubovik, O., North, P., and Fiedler, S.: Uncertainty in Aerosol Optical Depth From Modern Aerosol-Climate Models, Reanalyses, and Satellite Products, J. Geophys. Res.-Atmos., 127, e2021JD035483, https://doi.org/10.1029/2021JD035483, 2022. Wang, P., Yuval, J., and O’Gorman, P. A.: Non-Local Parameterization of Atmospheric Subgrid Processes With Neural Networks, J. Adv. Model. Earth Sy., 14, e2022MS002984, https://doi.org/10.1029/2022MS002984, 2022. Watson-Parris, D. and Smith, C. J.: Large uncertainty in future warming due to aerosol forcing, Nat. Clim. Change, 12, 1111– 1113, https://doi.org/10.1038/s41558-022-01516-0, 2022. Weber, N. J. and Mass, C. F.: Subseasonal Weather Prediction in a Global Convection-Permitting Model, B. Am. Meteorol. Soc., 100, 1079–1089, https://doi.org/10.1175/BAMS-D-180210.1, 2019. Wedi, N. P., Polichtchouk, I., Dueben, P., Anantharaj, V. G., Bauer, P., Boussetta, S., Browne, P., Deconinck, W., Gaudin, W., Hadade, I., Hatfield, S., Iffrig, O., Lopez, P., Maciel, P., Mueller, A., Saarinen, S., Sandu, I., Quintino, T., and Vitart, F.: A Baseline for Global Weather and Climate Simulations at 1 km Resolution, J. Adv. Model. Earth Sy., 12, e2020MS002192, https://doi.org/10.1029/2020MS002192, 2020. Wendisch, M., Poschl, U., Andreae, M. O., MacHado, L. A., Albrecht, R., Schlager, H., Rosenfeld, D., Martin, S. T., Abdelmonem, A., Afchine, A., Araujo, A. C., Artaxo, P., Aufmhoff, H., Barbosa, H. M., Borrmann, S., Braga, R., Buchholz, B., Cecchini, M. A., Costa, A., Curtius, J., Dollner, M., Dorf, M., Dreiling, V., Ebert, V., Ehrlich, A., Ewald, F., Fisch, G., Fix, A., Frank, F., Futterer, D., Heckl, C., Heidelberg, F., Huneke, T., Jakel, E., Jarvinen, E., Jurkat, T., Kanter, S., Kastner, U., Kenntner, M., Kesselmeier, J., Klimach, T., Knecht, M., Kohl, R., Kolling, T., Kramer, M., Kruger, M., Krisna, T. C., Lavric, J. V., Longo, K., Mahnke, C., Manzi, A. O., Mayer, B., Mertes, S., Minikin, A., Molleker, S., Munch, S., Nillius, B., Pfeilsticker, K., Pohlker, C., Roiger, A., Rose, D., Rosenow, D., Sauer, D., Schnaiter, M., Schneider, J., Schulz, C., De Souza, R. A., Spanu, A., Stock, P., Vila, D., Voigt, C., Walser, A., Walter, D., Weigel, R., Weinzierl, B., Werner, F., Yamasoe, M. A., Ziereis, H., Zinner, T., and Zoger, M.: Acridicon-chuva campaign: Studying tropical deep convective clouds and precipitation over amazonia using the New German research aircraft HALO, B. Am. Meteorol. Soc., 97, 1885–1908, https://doi.org/10.1175/BAMS-D-1400255.1, 2016. White, B., Gryspeerdt, E., Stier, P., Morrison, H., Thompson, G., and Kipling, Z.: Uncertainty from the choice of microphysics scheme in convection-permitting models significantly exceeds aerosol effects, Atmos. Chem. Phys., 17, 12145–12175, https://doi.org/10.5194/acp-17-12145-2017, 2017. https://doi.org/10.5194/acp-25-7789-2025 Atmos. Chem. Phys., 25, 7789–7814, 2025