Dataset for "Uncertainty in aerosol effective radiative forcing from anthropogenic and natural aerosol parameters in ECHAM6.3-HAM2.3" by Bhatti et al. (2025)

Uncertainty in aerosol effective radiative forcing from anthropogenic and natural aerosol parameters in ECHAM6.3-HAM2.3 Yusuf A. Bhatti1, Duncan Watson-Parris2, Leighton A. Regayre3,4, Hailing Jia1, David Neubauer5, Ulas Im6, Carl Svenhag6, Nick Schutgens7, Athanasios Tsikerdekis8, Athanasios Nenes5,9, Irfan Muhammed10, Bastiaan van Diedenhoven1, Ardit Arifi1, Guangliang Fu1, and Otto P. Hasekamp1 1SRON Space Research Organisation Netherlands, Leiden, Netherlands 2Scripps Institution of Oceanography and Halıcıo˘ glu Data Science Institute, University of California San Diego, La Jolla, CA, USA 3Met Office Hadley Centre, Exeter, Exeter, UK 4School of Earth and Environment, University of Leeds, Leeds, UK 5ETH Zurich, Zurich, Switzerland 6Aarhus University, Roskilde, Denmark 7Vrije Universiteit Amsterdam, Amsterdam, Netherlands 8KNMI, Utrecht, Netherlands 9Institute of Chemical Engineering Sciences, ICE-HT, Patras, Greece 10Department of Technical Physics, University of Eastern Finland, Kuopio, Finland Correspondence: Yusuf Bhatti (y[email protected]) Abstract. Interactions between aerosols, clouds, and radiation remain one of the largest sources of uncertainty in effective radiative forcing (ERF), limiting the accuracy of climate projections. Despite progress, key sources of parametric and structural uncertainty in aerosol–cloud and aerosol–radiation interactions remain poorly quantified. This study addresses this gap using a perturbed parameter ensemble (PPE) of 221 simulations with the ECHAM6.3-HAM2.3 climate model, varying 23 aerosol-5 related parameters that control emissions, removal, chemistry, and microphysics. The resulting global mean aerosol ERF is -1.24 W m−2(5-95 percentile: -1.56 to -0.89 W m−2). We find that uncertainty in aerosol ERF is dominated by sulfate-related processes, biomass burning, size, and natural emissions. Here, for Aerosol-Cloud Interactions, DMS and biomass burning emissions are important, whereas for Aerosol-Radiation Interactions, sulfate chemistry and dry deposition are also important. Despite structural differences across models, the leading causes of ERF uncertainty identified here align with findings from10 other PPEs. Comparison with satellite retrievals from POLDER-3/PARASOL reveals persistent model biases in aerosol optical depth (AOD), Ångström exponent (AE), and single-scattering albedo (SSA), many of which fall within the parametric uncertainty bounds of the PPE. Sulfate-related processes account for over 40% of AOD uncertainty, while AE and SSA uncertainties are strongly influenced by DMS, sea salt, and black carbon properties. PPEs can reduce some structural model biases through15 parameter adjustments, but others persist. These results highlight the need for combined efforts in parameter perturbation and structural model development to improve confidence in aerosol-forcing estimates and future climate projections. 1 https://doi.org/10.5194/egusphere-2025-2848 Preprint. Discussion started: 26 June 2025 c Author(s) 2025. CC BY 4.0 License. 1 Introduction Atmospheric aerosols play a critical role in Earth’s radiative energy balance (Forster et al., 2021; Myhre et al., 2013; Boucher et al., 2013). Aerosols directly influence the scattering (which cools) and absorption (which warms) of solar radiation (Li et al.,20 2022; Bellouin et al., 2020). Additionally, aerosols indirectly affect the radiative properties of clouds (Boucher et al., 2013; Haywood et al., 2021). Since the start of the industrial period, human activities have perturbed aerosol production, leading to significant changes in both the direct and indirect effects on global effective radiative forcing (ERF; Bellouin et al., 2020; Forster et al., 2021; Carslaw et al., 2013). Anthropogenic aerosol emissions have changed the global and regional aerosols, subsequently affecting clouds, precipitation,25 and thermodynamic properties (Carslaw et al., 2017; Karset et al., 2018; Bollasina et al., 2011; Deng et al., 2021). Despite their importance, the magnitude and variability of these aerosol effects remain significant sources of uncertainty in climate models. This uncertainty arises from the complex interactions between emissions, atmospheric processes, and cloud microphysics (Eidhammer et al., 2024; Yoshioka et al., 2019). Aerosol-cloud interactions (ACI) are challenging to model due to the complex and nonlinear processes that govern cloud30 microphysics and aerosol activation (Morrison et al., 2020). For instance, increases in cloud condensation nuclei (CCN) from anthropogenic emissions leads to a higher cloud droplet number concentration (Nd), causing brighter clouds (Twomey effect; Twomey, 1974). These changes can trigger further adjustments in clouds, such as shifts in cloud fraction and liquid water path, all of which contribute to the effective radiative forcing from aerosol-cloud interactions (ERFaci; Bellouin et al., 2020; Albrecht, 1989). Despite its crucial role in climate forcing, ERFaci remains the most uncertain component of ERF, with35 significant variations in its magnitude across different climate models (Zelinka et al., 2014; Gryspeerdt et al., 2020). With an ERF uncertainty from aerosols between -0.6 to -2 W m−2, aerosols are one of the largest sources of multi-model diversity when estimating climatic changes from the pre–industrial, as reported in IPCC Assessment report-6 (AR6) (Forster et al., 2021). Reducing this uncertainty would improve climate projections (e.g. Tsigaridis et al., 2014; Watson-Parris and Smith, 2022). Aerosol ERF estimates have improved since the Intergovernmental Panel for Climate Change (IPCC) Assessment40 Report 5 (AR5), in part due to improved observational studies (Myhre et al., 2013; Toll et al., 2019; Hasekamp et al., 2019b; Forster et al., 2021; Bellouin et al., 2020; Gryspeerdt et al., 2019; McCoy et al., 2020). Aerosol and cloud uncertainty in climate models is caused by a) how subgrid-scale processes such as cloud microphysics, convection, aerosol activation, and growth are coded using parameterizations (structural uncertainty), and b) the values assigned to parameters within these parametrizations. These parameters are often poorly constrained by observations, particularly for45 processes like sea-spray emissions, dimethyl sulfide emissions, and ice nucleation, which are highly differing among climate models (Morrison et al., 2020; Venugopal et al., 2025; Bhatti et al., 2023; Bock et al., 2021; Bhatti et al., 2022; Revell et al., 2021). This parameter uncertainty is partly an effect of prescribing global mean values to regionally varying processes, partly due to differences in other model structures like surface roughness and model dynamics, and partly caused by interactions and dependencies between model parameters. As a result, climate models have significant biases to present-day observations50 due to structural and parametric uncertainties (Regayre et al., 2023; Bhatti et al., 2024; Mortier et al., 2020). Furthermore, 2 https://doi.org/10.5194/egusphere-2025-2848 Preprint. Discussion started: 26 June 2025 c Author(s) 2025. CC BY 4.0 License. reconstructing pre–industrial aerosol conditions is challenging but critical for quantifying anthropogenic forcing (Carslaw et al., 2013, 2017; Hamilton et al., 2014). Causes of model uncertainty must be comprehensively quantified before observational constraints to ensure their individual and combined effects on aerosol ERF are well understood (Yoshioka et al., 2019; Carslaw et al., 2013; Lee et al., 2013a, 2011;55 Regayre et al., 2015, 2014). Perturbed parameter ensembles (PPEs) are a powerful tool for quantifying climate model parametric uncertainties, which have been performed with other models (e.g. UKESM1 and CESM2) (Eidhammer et al., 2024; Lee et al., 2013b; Regayre et al., 2018; Lee et al., 2011). Performing PPEs with various models is important to understand how structural differences in models affect ERF and present-day aerosol sensitivities. In this paper, we construct a PPE for the ECHAM6.3-HAM2.3 climate model and use machine learning statistical techniques to emulate a set of model variants, which60 we use to quantify the parametric uncertainty in model aerosol ERF. For this purpose, we perturb 24 parameters across 221 simulations, known to influence aerosoland cloud properties, emissions, and processes. We determine the relative contribution of different parameters to the ERF uncertainty through a variance-based sensitivity analysis. We also evaluate ensemble performance against satellite measurements and attribute specific causes for model uncertainty and bias. In Section 2, we describe the ECHAM6.3-HAM2.3 climate model configuration used here, and the experimental set-up of65 our perturbed parameter ensemble. Section 3.1 quantifies ERF uncertainties and attributes their respective causes. Section 3.2 uses satellite observations to compare and evaluate PPE uncertainties and model bias. 2 Methods 2.1 ECHAM6-HAM Model Simulations The simulations used in this work were performed by the ECHAM6.3-HAM2.3 (hereafter referred to as ’ECHAM6-HAM’)70 global aerosol–climate model. We use the default T63 horizontal resolution (1.875◦x 1.875◦) and 47 vertical layers (L47). We use the nudged configuration for which vorticity, divergence, and surface pressure are nudged towards the ERA-Interim reanalysis every 6hr, 48hr, and 24hr, respectively (Tegen et al., 2019; Zhang et al., 2012). The ECHAM6 component, developed at the Max Planck Institute for Meteorology, simulates large-scale atmospheric dynamics (Stevens et al., 2013). The HAM2 module represents various aerosol life cycle processes, including emissions, trans-75 port, deposition, and microphysical transformations (Stier et al., 2005; Zhang et al., 2012). Aerosol emissions come from both natural (sea salt, dust, and DMS) and anthropogenic (sulfate, black carbon, and organic carbon) sources, where the natural emissions are computed online, outlined in Neubauer et al. (2019), and the anthropogenic and biomass burning emissions are taken from ACCMIP (Lamarque et al., 2013). Aerosols are categorized into seven lognormal modes based on size and solubility, following the M7 scheme (Tegen et al.,80 2019; Vignati et al., 2004). Briefly, these modes include four soluble (nucleation, Aitken, accumulation, and coarse) and three insoluble (Aitken, accumulation, and coarse) categories. Each mode contains one or more aerosol species such as sulfate, black carbon, organic carbon, sea salt, and dust. Aerosols undergo microphysical processes such as nucleation, coagulation, 3 https://doi.org/10.5194/egusphere-2025-2848 Preprint. Discussion started: 26 June 2025 c Author(s) 2025. CC BY 4.0 License. condensation, and hygroscopic growth, influencing their size distribution and interactions with clouds (Schutgens and Stier, 2014).85 Cloud microphysics in ECHAM6-HAM is represented by a two-moment scheme, which prognostically computes cloud droplet and ice crystal number concentrations and mass (Lohmann and Roeckner, 1996). The stratiform cloud scheme simulates key processes such as condensation, autoconversion, accretion, evaporation, and aerosol scavenging, while ice-phase processes include heterogeneous nucleation, depositional growth, and the Wegener–Bergeron–Findeisen mechanism (Sundqvist et al., 1989; Lohmann and Roeckner, 1996; Stevens et al., 2013; Neubauer et al., 2019). Ice crystals and snowflakes are treated90 separately, with snow precipitating while ice crystals sediment within and outside of clouds. Convective clouds interact with stratiform clouds through detrainment processes (Lohmann and Hoose, 2009; Lohmann et al., 2008). Additional information regarding cloud microphysics, aerosol treatments, and process interactions can be found in Neubauer et al. (2019) and Tegen et al. (2019). 2.2 Simulations and Experimental Design95 To create the PPE, we performed model runs for 221 different parameter combinations for present-day and pre-industrial aerosol emissions. For the present-day simulations, we selected 2010 to align with the AEROCOM phase-III-control experiment and the availability of polarimetric aerosol retrievals from PARASOL (Hasekamp et al., 2024). For the pre-industrial calculations, we used aerosol emissions for the year 1850. All simulations were nudged to 2010 meteorology (wind fields) and used the same prescribed sea surface temperature. Each simulation underwent a 6-month spin-up phase, which was excluded100 from the analysis. The 221 simulations were used as input to an emulator (Section 2.3) to generate ensemble members, that sample combinations of parameter values across the full parameter space. To calculate the total aerosol ERF from its components due to aerosol-cloud (ERFaci) and aerosol-radiation interactions (ERFari), we computed the radiative differences between these ensembles (2010 and 1850). For regional analysis, each region is defined as in Jia et al. (2021), as shown in Figure S1. Finally, through a variance-based sensitivity analysis, we quantified the105 relative importance of each parameter globally and regionally (Saltelli et al., 2000). 2.2.1 Perturbed Parameters Both the Present-Day and Pre–Industrial simulation sets consist of 221 members, including the control run. Table 1 lists the 23 perturbed parameters along with the minimum and maximum values of their respective ranges. Each of the 220 ensemble members is assigned parameter values using Maximin Latin Hypercube sampling, which opti-110 mizes coverage of parameter combinations across the parameter space (Lee et al., 2013a; McKay et al., 1979). This method divides the possible range of each parameter into bins, ensuring that each sampled value falls within a unique bin. Once a value is assigned, subsequent samples cannot select from previously used bins. This approach guarantees that the full range of each parameter is explored while maintaining uniformly distributed marginal distributions. Parameter combinations are progressively selected to optimize the minimum Euclidean distance between points. Figure S2 shows the parameter ranges and115 their associated frequencies. The ranges were determined by "expert elicitation" based on the parameterization used. Their 4 https://doi.org/10.5194/egusphere-2025-2848 Preprint. Discussion started: 26 June 2025 c Author(s) 2025. CC BY 4.0 License. probability distribution frequencies were informed by a mixture of expert elicitation and prior PPE-constrained distributions in Yoshioka et al. (2019) and Eidhammer et al. (2024) estimates. These distributions are shown in Figure S2. In some instances, where explicitly stated, we also perturb CDNCmin, the minimum threshold for model cloud droplet concentrations, to highlight some of the structural and parametric uncertainties associated with clouds in ECHAM6-HAM. So,120 in places CDNCmin can be considered our 24th parameter, though we do not include it in Table 1 because its treatment is distinct from other parameters. We set the CDNCmin parameter to the fixed value of 40 cm−3, the default value in ECHAM6HAM recommended by Neubauer et al. (2019), to quantify aerosol ERF uncertainty and the relative importance of the 23 parameters in Table 1 as causes of model uncertainty. With 221 ensemble members and 23, or 24, perturbed parameters, our PPE has a simulation-to-parameter ratio of around125 10, which is much larger than recent PPE studies (Regayre et al. (2018) and Eidhammer et al. (2024) both use a ratio of 6). We cautiously use a high ratio of simulations to parameters to increase the density of parameter combinations in the parameter space, thereby enhancing the skill of Gaussian Process emulators (see Section 2.3). 2.3 Gaussian Process Emulator We use the Gaussian Process (GP) emulator from Earth System Emulator package (ESEm; Watson-Parris et al., 2021). GP130 was chosen through its recommendation by Eidhammer et al. (2024) and other PPE studies (e.g. Yoshioka et al., 2019). GP emulator is a powerful statistical model used here to emulate climate model outputs to sample the parameter space properly, needed for robust uncertainty quantification. Using climate model outputs to emulate model variants can be generated quickly and efficiently, reducing the need for extensive CPU hours and storage. We use GP emulators to create model variants across the 24-dimensional parameter space, all bounded by their minimum and maximum limits. In this study, emulators are applied135 to produce annual mean data for each model grid cell (when comparing to observations, only taking into account co-located data in space and time). Thus, we evaluate model uncertainty in aerosol ERF and its components across our model variants at each of around 96 x 192 model grid cells. 3 million model variants are obtained for ERF calculations, and 200,000 model variants are created for all other analyses. These sets of model variants are large enough to enable us to use variance-based sensitivity analyses to quantify the parametric causes of variance of each component (Lee et al., 2011).140 The uncertainties reported here are derived from the standard deviation and 5-95% credible range across these model variants. 2.4 Observational Datasets This study uses aerosol retrievals from the POLDER-3 instrument (Deschamps et al., 1994; Fougnie et al., 2007) aboard the PARASOL (Polarization and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from a Lidar)145 satellite for 2010. POLDER-3 was the only Multi-Angle Polarimeter (MAP) instrument in space between 2005 and 2013, providing multi-year in-orbit multispectral and multiangle photopolarimetric measurements of intensity and polarization. Each native ground pixel (6 km x 6 km) is measured under up to 16 viewing angles and at 6 wavelengths (443, 490, 565, 670, 865, 1020 nm). Cloud screening has been performed using a neural network cloud fraction approach (Yuan et al., 2024). 5 https://doi.org/10.5194/egusphere-2025-2848 Preprint. Discussion started: 26 June 2025 c Author(s) 2025. CC BY 4.0 License. Table 1. Table describing perturbed parameters used in this study, excluding the CDNCmin parameter. Control represents the value that is used for the control run. For the majority of parameters, a scaling factor compared to the control run values is applied, except the parameters indicated by ’Abs", where the actual parameter values are indicated. Variable Description Min Control Max EMI_FF Fossil fuel emissions. 0.5 1 2 EMI_ANTH_SO2Anthropogenic sulfur dioxide (SO2) emissions. 0.6 1 1.5 EMI_DMS Dimethyl sulfide (DMS) emissions. 0.33 1 3 EMI_SS Sea salt aerosol emissions (Long et al., 2011; Sofiev et al., 2011). 0.5 1 2.5 EMI_BB Biomass burning emissions. 0.25 1 4 EMI_BF Biofuel emissions. 0.25 1 4 EMI_DUST Dust emissions. 0.5 1 2 EMI_CMR_BF Biofuel emissions particle diameter (Abs). 25 30 100 EMI_CMR_BB Biomass burning emissions particle diameter (Abs). 25 75 100 EMI_CMR_FF Fossil fuel emissions particle diameter (Abs). 15 30 45 DRYDEP_AIT Dry deposition rate for Aitken-mode aerosols (Stier et al., 2005). 0.2 1 2 DRYDEP_ACC Dry deposition rate for accumulation-mode aerosols. 0.1 1 10 DRYDEP_COA Dry deposition rate for coarse-mode aerosols. 0.15 1 5 WETDEP_BC Below-cloud wet deposition rate (Croft et al., 2009, 2010). 0.5 1 2 WETDEP_IC In-cloud wet deposition rate (Croft et al., 2009, 2010). 0.75 1 1.25 BC_RAD_NI Black carbon imaginary refractive index (Abs). 0.2 0.71 0.9 DU_RAD_NI Dust aerosol imaginary refractive index (Abs). 0 0.001 0.01 SO2REACTIONS All sulfate chemistry scheme reaction rates, including DMS, SO2, SO4, and sulfate aqueous-phase reactions. 0.5 1 2 NUC_FT Nucleation rate in the free troposphere. 0.01 1 10 PH_PERT The initial hydrogen ion concentration for liquid-phase chemistry from (Feichter et al., 1996) (Abs). 4.5 5.6 7 KAPPA_SO4Hygroscopic parameter for sulfate aerosols - not used for cloud droplet activation (Abs). 0.4 0.6 0.8 KAPPA_SS Hygroscopic parameter for sea salt aerosols - not used for cloud droplet activation (Abs). 0.5 1 1.2 SO4COATING Layer thickness of sulfate to transfer an insoluble particle to a soluble mode (Abs) (aging; Vignati et al., 2004). 0.3 1 5 6 https://doi.org/10.5194/egusphere-2025-2848 Preprint. Discussion started: 26 June 2025 c Author(s) 2025. CC BY 4.0 License. The aerosol products derived from POLDER-3 measurements were retrieved by the Remote sensing of Trace gas and Aerosol150 Products (RemoTAP) algorithm (Hasekamp et al., 2024; Fu et al., 2025; Lu et al., 2022). While earlier aerosol retrieval studies with RemoTAP used a bi-modal aerosol description (Hasekamp et al., 2011) or a five-mode aerosol description (Fu et al., 2020; Fu and Hasekamp, 2018), the latest baseline in RemoTAP follows Lu et al. (2022), describing the aerosol size distribution by three log-normal modes, made up of one fine mode and two coarse modes (insoluble and soluble). The RemoTAP explicitly retrieves the aerosol layer height and the aerosol microphysical properties of effective radius, effective variance, column number,155 spherical fraction, the fractions of chemical component refractive index (real part of fine mode inorganic aerosol; imaginary part of fine mode black carbon and brown carbon; imaginary part of coarse mode dust; coarse mode hydrated sea salt). Based on the retrieved aerosol microphysical properties, the aerosol optical properties of multispectral AOD, SSA, and Ångström Exponent are calculated using the Mie-T matrix-improved geometrical optics model (Dubovik et al., 2006) along with their proposed spheroid aspect ratio distribution for computing optical properties for a mixture of spheroids and spheres. Several160 studies have demonstrated RemoTAP delivers accurate aerosol properties from MAP instruments (Hasekamp et al., 2024; Fu and Hasekamp, 2018; Fu et al., 2025; Schutgens et al., 2021). The data products used for evaluation are the Aerosol Optical Depth (AOD at 550 nm), Ångström Exponent (AE at 550865 nm), and Single-Scattering Albedo (SSA at 550 nm). The dataset is aggregated on a 1 ◦x 1 ◦grid, which is publicly available from https://public.spider.surfsara.nl/project/spexone/others/PARASOL/DATA/POLDER_1.0x1.0_basedon0.1_165 NPge2/. The AE and SSA retrieved for model comparison are filtered only including cases where AOD is greater than 0.20, to exclude measurement errors (Hasekamp et al., 2024). 3-hourly model outputs were used for comparison with satellite data. To effectively compare our model with the measurements, we co-locate the model with observations by picking the nearest grid cell for the model time closest to the satellite overpass. This will strongly mitigate temporal representation uncertainty, although the spatial representation uncertainty will170 still be present (Schutgens et al., 2016). Spatial uncertainties from POLDER are discussed in Section 3.2. 3 Results and Discussion 3.1 Uncertainty in effective radiative forcing Figure 1 shows the global 1850–2010 aerosol ERF, ERFaci, and ERFari probability density functions for our ECHAM6-HAM PPE. The emulated ERF has a mean of -1.24 W m−2with a 5-95 percentile credible range between -1.59 to -0.89 W m−2,175 representing an uncertainty range of 78% with respect to the mean ERF (Figure 1a). ERFaci has a mean of -1 W m−2(-1.30 to -0.73 W m−25-95 percentile range; Figure 1b). ERFari averages -0.27 W m−2, with a 5-95 percentile range between -0.48 and -0.09 W m−2(Figure 1c). Climate model PPEs , (ECHAM6-HAM; Yoshioka et al., 2019; Regayre et al., 2023; Eidhammer et al., 2024) and machine learning approaches (Albright et al., 2021; Smith et al., 2021) each sample different uncertainties based on their structural code base and the perturbations applied. For example, Regayre et al. (2018, 2023) sampled uncertainty180 in physical atmosphere model parameters in addition to aerosol parameters, so they have a wider range of aerosol ERF values than other approaches. Aerosol ERF in each study has a central tendency determined by the models that are used to create 7 https://doi.org/10.5194/egusphere-2025-2848 Preprint. Discussion started: 26 June 2025 c Author(s) 2025. CC BY 4.0 License. them, and the structural choices within these models. Summaries that additionally account for observational data uncertainties (e.g. IPCC, Forster et al., 2021; Bellouin et al., 2020) yield the largest ERF uncertainty. Aerosol ERF uncertainty is relatively low in the ECHAM6-HAM PPE, and falls fully within the Intergovernmental Panel on Climate Change’s Sixth Assessment185 Report (AR6) range of -2 to -0.6 W m−2(Forster et al., 2021), shown in Figure 1a. The relatively low uncertainty range may be caused by several factors, including a) our PPE only includes parametric uncertainty related to aerosol emissions, properties, and processes and excludes parameters relevant for droplet activation (e.g. updraft) b) aerosol ERF may be less responsive to aerosol perturbationsc within ECHAM6-HAM than other models and more dependent on cloud and radiation properties, or c) there may be some cancellation of positive and negative regional responses to parameter perturbations in global mean aerosol190 ERF calculations that suppress the uncertainty, as demonstrated by Regayre et al. (2015). Figure 2 presents the global distributions of ERF, ERFaci, and ERFari, along with their associated uncertainties. Our results show broad agreement in magnitude and spatial patterns of ERF and ERFaci with previous HadGEM-UKCA PPE studies (Yoshioka et al., 2019; Regayre et al., 2018, 2023), which employed relatively similar parameter perturbations for aerosol. However, those studies also perturbed cloud activation parameters, which may contribute to the differences. As shown in195 Figures 2b and 2d, the largest uncertainties in ERF correspond to regions with strong ERFaci signals. A notable distinction is that ECHAM6-HAM produces a stronger (more negative) ERF over land, whereas in HadGEM (Regayre et al., 2018), UKESM PPE (Regayre et al., 2023), and ACCMIP (Shindell et al., 2013a, Fig. 18), the strongest ERF occurs over marine regions dominated by persistent stratocumulus clouds. This divergence is attributable to structural characteristics of ECHAM6HAM. In particular, differences in sea salt emissions and aerosol water uptake and underestimated stratocumulus cloud cover200 all contribute to a weaker ERF over oceans compared to the other studies (Neubauer et al., 2019). Neubauer et al. (2019) showed a negative bias in shortwave (SW) radiation and net cloud radiative effects for ECHAM6-HAM, stemming from a pronounced negative stratocumulus bias over marine areas, particularly in the Pacific, which cannot be easily adjusted. This study is restricted to parametric uncertainty in ECHAM6-HAM and does not account for structural uncertainty. Additionally, the stronger land-based ERF arises from enhanced aerosol water uptake, differences in activation scheme sensitivity, and205 autoconversion rates, all of which intensify cloud albedo and contribute to a stronger cooling effect over continental regions. Regions of high anthropogenic activity since the pre—industrial era, such as Europe, Asia, and America, exhibit the strongest (most negative) ERF (Figure 2a), as expected. In Europe, the PPE mean ERF is -3.96 W m−2, primarily driven by ACI due to a high anthropogenic sulfur emission, with minor ARI contributions. Notably, the uncertainty in the ERF over Europe is relatively low, at 0.41 W m−2(a 5-95 percentile range of 39%). Asia also shows a substantial ERF, over -4 W m−2on average,210 largely due to ACI in China, which alone averages over -9 W m−2. Furthermore, ARI has a significant contribution to the total ERF over China as well. The ERF uncertainty over Asia has an uncertainty of 110%. Additionally, other regions characterized by high ERF values and significant uncertainty can be found in South America and Africa near industrialized anthropogenic and biomass burning emission sources (Figure 2). Sections 3.1.1 and 3.2 provide a more detailed examination of the factors contributing to these uncertainties.215 The ERF over the North Pacific Ocean is the highest of all the oceans at -5.8 W m−2, accompanied by a significant uncertainty of 0.58 W m−2(with a 5-95 percentile range of 147%). Structural cloud biases in ECHAM6-HAM cause the strongest 8 https://doi.org/10.5194/egusphere-2025-2848 Preprint. Discussion started: 26 June 2025 c Author(s) 2025. CC BY 4.0 License. 2 0 W m 2 Probability density function A Aerosol ERF 2 0 W m 2 Probability density function B Aerosol ERF ACI 2 0 W m 2 Probability density function C Aerosol ERF ARI ECHAM6-HAM PPE Forster et al . (2021) ( IPCC AR 6) Myhre et al. (2013) (IPCC AR5) Yoshioka et al. (2019) Bellouin et al. (2020) Regayre et al. (2023) Smith et al. (2021) Albright et al. (2021) Shindell et al. (2013) Figure 1. Global annual mean probability density functions between 1850 to 2010 of (a) effective radiative forcing (ERF), (b) ERFaci, (c) ERFari from perturbing aerosol parameters. 3 million model emulator-derived variants are used. Our 90% credible range is shown by the black line with the ’X’ marker presenting the median. Other estimations shown are using 90% credible ranges, with their marker representing their respective medians (Myhre et al., 2013; Yoshioka et al., 2019; Bellouin et al., 2020; Regayre et al., 2018; Smith et al., 2021; Albright et al., 2021; Forster et al., 2021). The two perturbed parameter ensemble papers, key motivators here, are highlighted in bold. For Shindell et al. (2013b), the 5th to 95th percentile values are calculated as multimodel mean plus and minus standard deviation times 1.645. . 9 https://doi.org/10.5194/egusphere-2025-2848 Preprint. Discussion started: 26 June 2025 c Author(s) 2025. CC BY 4.0 License. Arctic_Ocean Arctic_Land North_Pacific_Ocean North_America North_Atlantic_Ocean Europe Asia Tropical_Pacific_Ocean Tropical_Atlantic_Ocean Africa Tropical_Indian_Ocean South_Pacific_Ocean South_America South_Atlantic_Ocean South_Indian_Ocean Australia Antarctic_Ocean Antarctica Global 0 20 40 60 80 Percentage (%) (a) AOD Uncertainty % Arctic_Ocean Arctic_Land North_Pacific_Ocean North_America North_Atlantic_Ocean Europe Asia Tropical_Pacific_Ocean Tropical_Atlantic_Ocean Africa Tropical_Indian_Ocean South_Pacific_Ocean South_America South_Atlantic_Ocean South_Indian_Ocean Australia Antarctic_Ocean Antarctica Global (b) AE Uncertainty % Arctic_Ocean Arctic_Land North_Pacific_Ocean North_America North_Atlantic_Ocean Europe Asia Tropical_Pacific_Ocean Tropical_Atlantic_Ocean Africa Tropical_Indian_Ocean South_Pacific_Ocean South_America South_Atlantic_Ocean South_Indian_Ocean Australia Antarctic_Ocean Antarctica Global 0 20 40 60 80 Percentage (%) (c) SSA Uncertainty % EMI_BB EMI_CMR_BB BC_RAD_NI EMI_FF EMI_DMS WETDEP_IC SO4_COATING EMI_CMR_FF DU_RAD_NI EMI_ANTH_SO2 SO2_REACTIONS EMI_SS EMI_DUST DRYDEP_ACC KAPPA_SO4 NUC_FT DRYDEP_AIT DRYDEP_COA EMI_CMR_BF EMI_BF PH_PERT WETDEP_BC KAPPA_SS Figure 5. 200,000 model variants and their respective contribution of uncertainties from individual parameters in present-day (a) AOD, (b) AE, (c) SSA. Only parameters above 5% are shown. No hatches represent parameters perturbing aerosol emissions. Diagonal hatching represents microphysics parameters, crossed hatches represent chemistry perturbing parameters, and horizontal hatches show the emitted diameter-sized parameters. 16 https://doi.org/10.5194/egusphere-2025-2848 Preprint. Discussion started: 26 June 2025 c Author(s) 2025. CC BY 4.0 License. Table 2. Percentage contribution to global annual mean ERF uncertainty by individual parameters over 200,000 model variants. Parameter Uncertainty Contribution (%) EMI_FF 19.25 EMI_ANTH_SO2 14.75 EMI_CMR_BB 12.66 EMI_BB 11.33 EMI_DMS 9.53 SO2_REACTIONS 8.13 EMI_SS 5.98 NUC_FT 4.16 EMI_CMR_FF 3.70 BC_RAD_NI 3.55 DU_RAD_NI 2.45 DRYDEP_ACC 1.26 SO4_COATING 1.05 EMI_DUST 0.97 KAPPA_SO4 0.41 DRYDEP_COA 0.25 WETDEP_IC 0.21 EMI_BF 0.12 KAPPA_SS 0.09 EMI_CMR_BF 0.07 DRYDEP_AIT 0.04 WETDEP_BC 0.02 PH_PERT 0.02 3.2.1 Present-day AOD320 The PPE mean co-located global average AOD is 0.12 (Figure 6b and Figure 4a). The PPE has a 5-95% percentile range between 0.09 and 0.16, whereas the POLDER mean of 0.15 (±0.04 over land; ±0.03 over ocean; Hasekamp et al. (2024)) is close to the high end of the PPE range (see Figure 6a and Figure 4a). Global averages of AOD in the PPE are underestimated compared to observations (Figure 6d and Figure 4a). The largest statistical differences between the model and observations are primarily found over land, where the model tends to underestimate AOD (Figure 6b). The few regions of positive bias over land325 are found over China and Northern Africa. Over the ocean, there are more regions with positive AOD bias (Figure 6b), but at higher latitude oceans, there is a significant negative bias (Figure 6b). Regions with high absolute AOD uncertainty correspond to areas with significant differences between the model and observational data, as shown in Figure 6d. This observation is consistent with comparisons to MODIS AOD (Figure S7). 17 https://doi.org/10.5194/egusphere-2025-2848 Preprint. Discussion started: 26 June 2025 c Author(s) 2025. CC BY 4.0 License. Differences between the model and observations in Figure 6b are most negative over Africa, India, and North America,330 which are major dust sources. Additionally, AOD over China/East Asia is positively biased in ECHAM6-HAM (Figure 6b). Generally, marine regions have larger AOD values in the PPE than in observations, except the Southern Ocean, which is negatively biased. Dust is a major source of uncertainty in our PPE, as shown in Figure 5a. Dust is a known bias for larger aerosol particles in the ECHAM6-HAM model (Tegen et al., 2019). The uncertainty in Africa primarily arises from dust and biomass burning335 emissions (EMI_DUST and EMI_BB), as illustrated in Figure 5a. Notably, the bias observed over Africa (Figure 6b) is greater than the uncertainty indicated by our PPE (Figure 6d). To address this bias, we would need to consider additional parameters, such as dust size, which are not included in this analysis. Over East Asia, there is a positive bias in AOD (Figure 6b), consistent with findings by Salzmann et al. (2022). This bias is attributed to a diverse range of aerosol species from anthropogenic sources, with sulfate parameters being the primary con-340 tributors (Yang et al., 2024). The substantial uncertainty in our PPE over China (Figure 6d) may offset this bias, as the AOD uncertainty is comparable in magnitude to the bias. This uncertainty is mainly due to sulfate-related parameters, including EMI_ANTH_SO2, Kappa_SO4, SO2Reactions, EMI_FF, and DRYDEP_ACC (see Figure 4 and Figure 5a). The bias in this region has been previously identified by Tsikerdekis et al. (2023). Over this region, increasing the scale factors for SO2Reactions and KAPPA_SO4enhances AOD uncertainty (Figure 5a and Figure S4) through increasing sulfate hygroscopicity and lifetime,345 exacerbating existing model overestimates in hydrophilic aerosol growth (Tsikerdekis et al., 2023). These parameters probably can compensate to some extent for the relative humidity bias found by Tsikerdekis et al. (2023) in structural uncertainty. Oceanic regions that have high AOD uncertainty coincide with regions of positive AOD bias are shown in Figure 6c. Salzmann et al. (2022) attributes this bias to the lack of shallow convective clouds precipitating in ECHAM6-HAM, therefore resulting in a lack of wet removal of hydrophilic aerosol by these clouds, which are frequent in these regions. Our PPE also350 identifies this bias through parametric uncertainties (Figure 6d), presenting a possibility which may compensate for this structural inadequacy related to the convective scheme (Salzmann et al., 2022). Additionally, Tsikerdekis et al. (2023) identified that the regions of heightened relative humidity bias coincide with the oceanic regions in our PPE that are both positively biased (Figure 6c) and have notably large AOD uncertainty (Figure 6e). As a result, excess modeled relative humidity enhances hygroscopic aerosol growth, increasing AOD without the important convective removal mechanism. Our PPE suggests that this355 growth primarily comes from sea salt aerosols, as shown in Figure S4. Interestingly, these regions of high absolute uncertainty are not as prominent in the relative uncertainty field (Figure 6e). This suggests that absolute uncertainty is more sensitive to the magnitude of AOD itself, whereas relative uncertainty highlights regions where uncertainty is large relative to AOD. This distinction indicates that different processes may control absolute and relative uncertainty, particularly over low AOD regions such as the Southern Ocean, emphasizing the need to assess both metrics when evaluating model uncertainty and observational360 constraints. Figure 5a shows that sea salt and DMS emissions cause the highest uncertainties in AOD (37%). This is partly because sea salt and DMS emissions have much wider areas to emit and thus contribute to AOD uncertainty over most regions (Figure S4). The parameters that control much of the AOD uncertainty over land (Asia and Europe) mostly are from anthropogenic activities 18 https://doi.org/10.5194/egusphere-2025-2848 Preprint. Discussion started: 26 June 2025 c Author(s) 2025. CC BY 4.0 License. Figure 6. Aerosol Optical Depth (AOD 550 nm) global (a) POLDER instrument measurement, (b) difference between the PPE and POLDER, (c) PPE mean, (d) PPE absolute uncertainty (2σ), and (e) PPE relative uncertainty during 2010. PPE simulations are co-located to the same coordinate and time as POLDER. Stippling indicates where the difference is not statistically significant at the (95% level of confidence, Student’s t-test). (Figure 3a). Depositional parameters, such as WETDEP_IC and DRYDEP_ACC, have a globally uniform contribution to the365 uncertainty (of around 11% and 8%, respectively) across all regions. Figures 6c and 4 show a slightly lower emulated AOD than that observed over the Southern Ocean. The relative AOD uncertainty over the Southern Ocean is higher than any other region (Figure 6d). The high relative importance of sea spray and DMS emissions aligns with Yoshioka et al. (2019) and Carslaw et al. (2013), which highlight the substantial uncertainty associated with natural aerosol emissions, also present spatially in the ECHAM6-HAM model. Excluding Antarctica, the370 relative uncertainty in AOD over the Southern Ocean is 33%, higher than the global average of 23%. Sea spray and DMS emissions cause around 45% of the Southern Ocean AOD uncertainty. Overall, our results reveal that present-day AOD (Figure 5a) and ERF (Figure 3a) have three common sources of uncertainty in the top seven uncertainties (EMI_DMS, EMI_SS, SO2Reactions). This is consistent with the work of Regayre et al. (2018), which suggests that although both AOD and aerosol forcing have some influence from common aerosol properties, their main375 sources of variability differ due to distinct processes, as found in Lee et al. (2016). For example, sea salt emissions cause significant perturbation to AOD uncertainty, whereas sea salt emissions only perturb ERF uncertainty by 6%. Conversely, biomass burning-related parameters cause substantial perturbation to the ERF uncertainty (Figure 3), but not so for AOD uncertainty (Figure 5). Thus, observational constraints to match present-day AOD will not guarantee constraints to all parameters relevant to ERFaci, even though they may still provide a useful constraint on ERFari (Watson-Parris et al., 2020).380 19 https://doi.org/10.5194/egusphere-2025-2848 Preprint. Discussion started: 26 June 2025 c Author(s) 2025. CC BY 4.0 License. 3.2.2 Present-day Ångström Exponent The global Ångström Exponent (AE) from the ECHAM6-HAM PPE exhibits an overestimation relative to PARASOL observations, particularly over oceanic regions and high latitudes (Figures 7b and 4b). This suggests that particle sizes in the ECHAM6-HAM model are too small relative to observations. The model particularly underestimates AE over dust emissions in Northern Africa and over parts of East China, although it should be noted that POLDER AE has a positive bias over Dust385 regions (Hasekamp et al., 2024). Strong positive AE bias is found over the Southern Ocean, the Americas, and large parts of Asia and Australia. Uncertainty attribution in Figure 5b reveals that key parameters controlling AE uncertainty globally are mostly EMI_DMS, EMI_SS, SO2Reactions, and NUC_FT. Some parameters, such as SO2Reactions, NUC_FT and EMI_BF contribute almost uniformly to AE uncertainty in all regions. These drivers contrast with AOD uncertainty (Figure 5a), which is primarily linked390 to perturbations in aerosol mass emissions, highlighting the distinct sensitivity of AE to processes affecting particle size and mixing state. Regions with the largest AE biases, such as high latitudes, coincide with high parametric AE uncertainty (Figure 7d). Over the regions of AE uncertainty larger than 0.1 (Figure 7c), SO2Reactions, SO4_Coating, EMI_DMS, and EMI_SS collectively account for almost 50% of the uncertainty (Figure 5b). The dominance of sulfate-related parameters underscores the critical395 role of aerosol aging and growth related processes in modulating size distributions. Over land, SO4_Coating, EMI_DUST, and SO2Reactions drive uncertainty, whereas oceanic regions are influenced most by sea salt emissions (EMI_SS) and DMS oxidation pathways (SO2Reactions and EMI_DMS) (Figure 5b; Figure S5). Particle aging (SO4_Coating) has a particularly large regional impact on AE uncertainty (Figure 5b) as sulfate coatings modify particle hygroscopic growth. Sulfate coatings (SO4_Coating) enhance particle hygroscopicity, accelerating water up-400 take and growth of fine-mode aerosols, which systematically reduces AE in regions like the Southern Ocean, and thus increases its parametric uncertainty (Figure 5b). The overlap of SO2Reactions in both AOD and AE uncertainties (Figure 5) highlights the important role of sulfate in both mass and size regulation. SO2Reactions and EMI_SS influence the mixing state and hygroscopic growth of aerosol processes, which are critical for determining the aerosol optical properties. Tegen et al. (2019) and Salzmann et al. (2022) emphasize that the underrepresentation of coarse-mode particles, especially sea salt, can lead to405 substantial overestimations of AE, which is present in the comparison with the PARASOL AE (Figure 7b). The interplay between model biases (Figure 7b) and parametric uncertainty (Figure 7d) is amplified in the Southern Ocean, due to the lack of coarse model sea salt particles, which are strongly present over the Southern Ocean (Venugopal et al., 2025). A pronounced positive AE bias (Figure 7b) aligns with an underestimation of coarse-mode sea salt aerosols. This corresponds to a lower AOD (Figure 6c), suggesting that a reduction in aerosol mass may influence AE and AOD biases. Given the region’s410 high proportion of sulfate and sea salt, uncertainties in EMI_SS, EMI_DMS, and SO2Reactions (Figure 5b) likely propagate through both size distribution and mass, amplifying uncertainty (Figure 6c). This is consistent with global model challenges in representing marine aerosol emissions and aging (Tegen et al., 2019). Reducing these uncertainties will require improved constraints on sea salt flux parameterizations and DMS-to-sulfate conversion pathways. 20 https://doi.org/10.5194/egusphere-2025-2848 Preprint. Discussion started: 26 June 2025 c Author(s) 2025. CC BY 4.0 License. Figure 7. As in Figure 6, but showing Ångström Exponent. In contrast, North Africa exhibits a negative AE bias (Figures 7c and 4b), suggesting the model overestimates dust aerosol415 size. This conflicts with prior ECHAM6-HAM validations (Tegen et al., 2019), and might be related to a positive AE bias in PARASOL (Hasekamp et al., 2024). Our PPE mean AE (0.67) here aligns with earlier studies, yet the region shows large uncertainty tied to EMI_DUST (Figure 5b and Figure S5). The similarity in magnitude between the AE bias and the uncertainty associated with dust emissions suggests that perturbations in EMI_DUST could account for the observed bias. ECHAM6-HAM exhibits a strong positive AE bias over the Intertropical Convergence Zone (ITCZ) and the South Pacific420 Convergence Zone (SPCZ; Figure 7c). This aligns with previously identified precipitation biases linked to cloud cover and ice water path uncertainties (Neubauer et al., 2019; Stevens et al., 2013), as structural uncertainties in cloud parameterization and parametric drivers like WETDEP_IC amplify biases by reducing coarse-mode sea salt via excessive wet deposition. Furthermore, EMI_SS and EMI_DMS uncertainties disrupt sulfate-driven aerosol growth (Figure S5; Croft et al., 2009). The model’s underestimation of sea salt particle size, dominant in these regions, exacerbates the AE overestimation (Figure 7c), as425 DMS-derived sulfate condenses onto fewer coarse particles (Salzmann et al., 2022). 21 https://doi.org/10.5194/egusphere-2025-2848 Preprint. Discussion started: 26 June 2025 c Author(s) 2025. CC BY 4.0 License. Figure 8. As in Figure 7 but showing Single Scattering Albedo. 3.2.3 Present-day SSA The global PPE reveals a systematic overestimation of single-scattering albedo (SSA) over oceans relative to PARASOL observations (Figures 8b and 4c), indicating that modeled marine aerosols are less absorptive than from PARASOL retrievals. The model presents a negative SSA bias around central Africa and parts of South East Asia and India, whereas a strong positive430 bias is present over Australia and parts of the Arctic region. SSA uncertainty from the PPE (Figure 8d) is insufficient to account for the strong positive bias over the oceans and Australia. However, in regions with negative SSA bias (such as central Africa, South East Asia, and India, Figures 8b), the model uncertainty is larger than the bias itself; therefore, the observed underestimation falls within the ensemble’s range of variability. Uncertainty attribution identifies BC_RAD_NI as the largest global contributor to SSA uncertainty (Figure 5c), reflecting its435 direct control over BC absorption efficiency. Secondary drivers include EMI_DMS, EMI_SS, and SO2Reactions, particularly over the Southern Ocean, where marine aerosol processes outweigh BC’s influence (Figure 5c, Figure S6)). Across most land regions, BC_RAD_NI and DU_RAD_NI account for approximately 20% and 15% of SSA uncertainty, respectively (Figure 5c). These parameters govern the imaginary refractive indices of black carbon (BC) and dust, directly influencing their absorptive properties.440 22 https://doi.org/10.5194/egusphere-2025-2848 Preprint. Discussion started: 26 June 2025 c Author(s) 2025. CC BY 4.0 License. SSA uncertainty over the ocean stems from marine emissions (e.g., EMI_SS, EMI_DMS), whereas terrestrial uncertainty is dominated by biomass burning (EMI_BB and BC_RAD_NI), dust (EMI_DUST and DU_RAD_NI), and sulfate chemistry (SO2Reactions) (Figure 5c). The parametric uncertainty in SSA over land is roughly twice as large as over oceans (Figure 8d and Figure 4b). Much of the BC_RAD_NI contributions over ocean are related to outflow of existing aerosol emissions from the land (Figures 5c, Figures S6). Over regions like South America and Africa, where biomass burning is a dominant aerosol445 source, a negative SSA bias (Figure 8c) may suggest modeled BC is absorbing too strongly relative to observations (Salzmann et al., 2022), or has a bias in the ratio between BC and OC (Organic Carbon). These regions also show elevated uncertainty tied to BC_RAD_NI, EMI_BB, SO2Reactions, and DU_RAD_NI (Figure 5c, Figure S6), underscoring the need for better constraints on BC and dust optical properties and combustion emission fluxes. 3.2.4 Model development priorities related to Present-Day aerosol biases450 Sulfate-related parameters cause 40% of AOD and SSA uncertainty, and 44% for AE uncertainty. For the most part, this is from the shared influence of SO2Reactions and EMI_DMS across AOD, AE, and SSA, highlighting sulfate’s key role in present-day aerosol uncertainty. Regional patterns also highlight how key sources of AOD uncertainty cascade to uncertainties in AE and SSA. Over the Southern Ocean, for example, the elevated AOD relative uncertainty stems from sea salt emissions (EMI_SS) and DMS-derived sulfate production (EMI_DMS), which have smaller size distributions than observations (Figure 7b), am-455 plifying AE biases and uncertainty (Figure 7b, d). Here, marine emissions (EMI_SS, EMI_DMS) drive both SSA and AE uncertainties, while their underrepresentation also reduces AOD (Figure 6c). As a result, we suggest improvements in the treatment of sulfate and marine aerosol emissions (e.g., EMI_DMS, EMI_SS) with a focus on particle size distributions to reduce simultaneous AOD, and AE biases. Additionally, future work with subdivide sulfate-related parameters further to better capture and constrain sulfates global uncertainty sources.460 Parametric uncertainties may be able to compensate for some structural uncertainties in present-day aerosol. For example, relative humidity causes problems to aerosol growth in ECHAM6-HAM, relative to observations (Tsikerdekis et al., 2023). Additionally, the model has an AOD bias over marine regions from structural problems related to the convective scheme (Salzmann et al., 2022). Through perturbing sulfate and sea salt parameters, aerosols may grow over China and marine regions enough to compensate for the AOD bias. Additionally, the negatively biased modeled SSA over central Africa overlaps with the PPE465 uncertainty from BC_RAD_NI, enabling the possibility of mitigating SSA bias through aerosol refractive index parameters. Structural uncertainties in cloud parameterization may be compensated by depositional parametric drivers, like WETDEP_IC, which affect large aerosol particles. Parameters related to sulfate, BC_RAD_NI, and wet deposition may be important for future tuning, as they could partially offset known structural deficiencies related to humidity and cloud interactions. AE uncertainties from the PPE have very little overlap with the modeled bias relative to POLDER. Some parameters, such470 as NUC_BL and EMI_BF have global contributions to AE uncertainty. Many regions with the largest AE biases, such as high latitudes, coincide with high parametric AE uncertainty, but the extent is insufficient to account for the AE bias. The cause of this mismatch is likely from structural uncertainty stemming from issues (a lack of) in the larger aerosol size distributions, as shown by Tegen et al. (2019). Perturbing aerosol mass may enhance AE and AOD uncertainties to overlap biases. Therefore, 23 https://doi.org/10.5194/egusphere-2025-2848 Preprint. Discussion started: 26 June 2025 c Author(s) 2025. CC BY 4.0 License. future PPEs will extend this PPE design to include parameters influencing larger aerosol size modes, possibly through direct475 perturbation of aerosol mass or size parameters. 4 Discussion and Conclusion Using a perturbed parameter ensemble (PPE) with the ECHAM6-HAM model, we have quantified aerosol-related parametric uncertainties in aerosol effective radiative forcing (ERF) and present-day aerosol properties (AOD, AE, SSA). Our PPE explores 23 parameters that influence aerosol emissions, removal processes, chemistry, and microphysics, building further on the480 approach of Yoshioka et al. (2019) developed for the HadGEM model. We also include newly identified parameters informed by recent research and model-specific biases, such as sulfate chemistry. This PPE framework enables us to assess uncertainties at both global and regional scales. Ultimately, our goal is to use PPEs to identify and constrain key ERF causes and uncertainties and to build confidence in climate projections by clarifying the role of parameter uncertainty and their potential to identify and/or reduce structural model uncertainties. When comparing a PPE across multiple models, structural inadequacies may be485 overcome by re-tuning model parameters. Despite large substantial structural differences between two models (ECHAM6HAM and HadGEM), the magnitude and dominant parametric sources of aerosol ERF uncertainty overlap in agreement across the ensembles. Aerosol-related parametric uncertainties within our PPE lead to an aerosol ERF with a 5-95 percentile uncertainty range between -1.59 W m−2to -0.89 W m−2. Sulfate-related parameters (sulfate chemistry, Anthropogenic SO2emissions, DMS490 emissions) cause 57% of global ERF uncertainty, and biomass burning parameters (EMI_CMR_BB, BC_RAD_NI, EMI_BB) 27.5%. While sulfate dominates aerosol forcing in many regions, biomass burning plays a significant role in regions with high biomass burning activity, like Africa. The smaller particle size of black carbon determined from the AE bias, combined with biases in its refractive index (black carbon imaginary refractive index), contributes to an uncertainty in absorption properties and radiative effects.495 Global AOD uncertainties are primarily caused by natural aerosol emissions (DMS and sea salt) (33%) and depositional processes (22%). Sulfate-related parameters contribute over 40% of the uncertainty in present-day AOD, AE, and SSA, underscoring their central role in shaping both aerosol climatology and ERF. Regions with high AOD uncertainty in ECHAM6-HAM often coincide with areas where model biases relative to observations are largest. Many of these regions have a positive AE bias, suggesting an underrepresentation of coarse-mode particles, as also identified by Tegen et al. (2019). ECHAM6-HAM500 under-represents coarse-mode aerosols over much of the ocean, particularly the Southern Ocean. Biomass-burning aerosols in ECHAM6-HAM appear smaller and more absorbing than observed by PARASOL, leading to a positive SSA bias, particularly over Africa and South America, although it should be noted that PARASOL has considerable SSA observational uncertainty. The main causes of uncertainty in ERF do not show full overlap with those in present-day AOD, AE, and SSA. On the other hand, the different causes of uncertainties between AOD, AE, and SSA in different regions suggest that spatially resolved505 measurements of these three aerosol properties together can help in constraining parameters that dominate ERF uncertainty, depending on the accuracy of the observations. However, some important causes of ERF uncertainty (most notably the emitted 24 https://doi.org/10.5194/egusphere-2025-2848 Preprint. Discussion started: 26 June 2025 c Author(s) 2025. CC BY 4.0 License. particle sizes of BB and FF) do not show similar causes of uncertainties in AOD, AE, or SSA. Adding observations of CDNC in addition to aerosol properties is expected to help constrain those parameters, which strongly impact ACI (McCoy et al., 2020). Future work will aim to constrain the ECHAM6-HAM ERF based on the recent satellite retrievals from the PACE (Werdell510 et al., 2019; Hasekamp et al., 2019a; Fu et al., 2025) and EarthCARE (Illingworth et al., 2015) missions launched in 2024, which provide the relevant aerosol and cloud observations with unprecedented accuracy. Many of the model biases that appear when comparing with observations are caused by a combination of parametric uncertainties and structural model uncertainties. The latter may be partially mitigated through parameter tuning; however, this compensation may mask rather than resolve model deficiencies. To deepen our understanding of the different structural model515 uncertainties, a multi-model PPE is needed. The tight clustering of ERF uncertainty sources across models suggests that efforts to constrain ERF may benefit from cross-model coordination, especially around sulfate chemistry, DMS emissions, and biomass burning representation. By applying similar perturbations across multiple models (e.g., ECHAM6-HAM, HadGEM, ICON-HAM, EC-Earth, and NorESM), it would be possible to isolate uncertainties stemming from missing processes and those arising from poorly constrained parameter values. A multi-model PPE is important for CMIP7 to address the model520 structural uncertainties’ contribution to ERF uncertainties and provide a more robust calculation for ERF. Furthermore, this would provide actionable information for improving structural representations of key aerosol processes, leading to improved model skill at simulating climate change. 5 Data Availability All model simulation data are archived and are available by contacting the corresponding author. Parameter values are found525 in Zenodo, and annual mean PPE diagnostics are available in https://doi.org/10.5281/zenodo.15640132 (Bhatti et al., 2025). POLDER observations can be found in https://public.spider.surfsara.nl/project/spexone/others/PARASOL/DATA/POLDER_ 1.0x1.0_basedon0.1_NPge2/2010/. Additionally, the POLDER measurements combined with AOD, AE, and SSA co-located PPE data can be obtained in Zenodo https://doi.org/10.5281/zenodo.15640132. For higher frequency PPE data (monthly, daily, 3-hourly) or additional PPE diagnostics, contact the corresponding author. The ESEm code used to emulate the simulations is530 in https://doi.org/10.5281/zenodo.5466563 (Watson-Parris et al., 2021). Author contributions. Author contributions. YAB implemented the model setup, performed model simulations, and wrote the manuscript, with assistance from all co-authors. DWP, LAR, HJ, DN, NS, and OPH assisted in technical model setup, methods, and provided expertise in analysis. 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