Accounting for the condensation of organic vapors along with water vapor (co-condensation) has been shown in adiabatic cloud parcel model (CPM) simulations to enhance the number of aerosol particles that activate to form cloud droplets. The boreal forest is an important source of biogenic organic vapors, but the role of these vapors in co-condensation has not been systematically investigated. In this work, the environmental conditions under which strong co-condensation-driven cloud droplet number enhancements would be expected over the boreal biome are identified. Recent measurement technology, specifically the Filter Inlet for Gases and AEROsols (FIGAERO) coupled to an iodide-adduct chemical ionization mass spectrometer (I-CIMS), is utilized to construct volatility distributions of the boreal atmospheric organics. Then, a suite of CPM simulations initialized with a comprehensive set of concurrent aerosol observations collected in the boreal forest of Finland during spring 2014 is performed. The degree to which co-condensation impacts droplet formation in the model is shown to be dependent on the initialization of temperature, relative humidity, updraft velocity, aerosol size distribution, organic vapor concentration, and the volatility distribution. The predicted median enhancements in cloud droplet number concentration (CDNC) due to accounting for the co-condensation of water and organics fall on average between 16 % and 22 %. This corresponds to activating particles 10–16 nm smaller in dry diameter that would otherwise remain as interstitial aerosol. The highest CDNC enhancements (ΔCDNC) are predicted in the presence of a nascent ultrafine aerosol mode with a geometric mean diameter of ∼ 40 nm and no clear Hoppel minimum, indicative of pristine environments with a source of ultrafine particles (e.g., via new particle formation processes). Such aerosol size distributions are observed 30 %–40 % of the time in the studied boreal forest environment in spring and fall when new particle formation frequency is the highest. To evaluate the frequencies with which such distributions are experienced by an Earth system model over the whole boreal biome, 5 years of UK Earth System Model (UKESM1) simulations are further used. The frequencies are substantially lower than those observed at the boreal forest measurement site (< 6 % of the time), and the positive values, peaking in spring, are modeled only over Fennoscandia and the western parts of Siberia. Overall, the similarities in the size distributions between observed and modeled (UKESM1) are limited, which would limit the ability of this model, or any model with a similar aerosol representation, to project the climate relevance of co-condensation over the boreal forest. For the critical aerosol size distribution regime, ΔCDNC is shown to be sensitive to the concentrations of semi-volatile and some intermediate-volatility organic compounds (SVOCs and IVOCs), especially when the overall particle surface area is low. The magnitudes of ΔCDNC remain less affected by the more volatile vapors such as formic acid and extremely low- and low-volatility organic compounds (ELVOCs and LVOCs). The reasons for this are that most volatile organic vapors condense inefficiently due to their high volatility below the cloud base, and the concentrations of LVOCs and ELVOCs are too low to gain significant concentrations of soluble mass to reduce the critical supersaturations enough for droplet activation to occur. A reduction in the critical supersaturation caused by organic condensation emerges as the main driver of the modeled ΔCDNC. The results highlight the potential significance of co-condensation in pristine boreal environments close to sources of fresh ultrafine particles. For accurate predictions of co-condensation effects on CDNC, also in larger-scale models, an accurate representation of the aerosol size distribution is critical. Further studies targeted at finding observational evidence and constraints for co-condensation in the field are encouraged.

The contribution of natural aerosol particles from boreal forests to total aerosol loadings may increase with reduction in anthropogenic emissions. Aitken and accumulation mode particles in boreal regions differ significantly in hygroscopicity, and ignoring this size dependence can cause large uncertainty in Cloud Condensation Nuclei (CCN) prediction. We applied κ-Köhler theory to a multi-year dataset (2016–2020) from Hyytiälä, Finland, to evaluate different representations of aerosol chemical composition for CCN prediction. Overpredictions by forward closures using either bulk chemical composition from an Aerosol Chemical Speciation Monitor (ACSM) or a constant κ= 0.18 were mitigated to a great extent by optimizing size-resolved composition using two inverse modeling approaches: (1) Nelder–Mead method with the size distribution fixed to its median during each 2 h CCN measurement cycle, and (2) MCMC (Markov Chain Monte Carlo) accounting also for the variability in the size distribution during each cycle. Both methods improved closure at SS =  0.2 %–1.0 % (with Geometric Mean Bias GMB values 1.12–1.20 and 0.95–1.05, respectively), with moderate improvement at 0.1 % (GMBs of 1.53 and 1.32, respectively). The Aitken mode was enriched in organics in 77 % of cases using method (1) and 46 % using method (2) – with typical κ values of ∼ 0.1 for Aitken and ∼ 0.3 for accumulation modes. The results generally align with known size-dependent chemical composition in Hyytiälä and indicate that variability in CCN hygroscopicity is largely driven by Aitken mode composition. Our results demonstrate the potential of inverse CCN closure methods for obtaining valuable information of the size-dependent chemical composition.

Boreal forests contribute to climate cooling through aerosol–cloud interactions. As rising temperatures are expected to modify the aerosol emissions, an improved understanding of aerosol activation and cloud microphysical properties over boreal regions is required. Here, we present, for the first time, long-term statistics of the maximum parcel supersaturation(SS_max) and the corresponding cloud droplet number concentration at the SSmax height level (CDNC_max) in adiabatic warm clouds. We use a comprehensive multiyear (2014–2024) observational dataset of cloud-base updraft velocity from warm, non-precipitating clouds, together with aerosol size-distribution and chemical composition measurements from Station for Measuring Ecosystem–Atmosphere Relations II (SMEAR II) measurement site in Hyytiälä, Finland, to perform cloud parcel model(CPM) simulations. We find median values of SS_max = 0.23% and CDNC_max = 213 cm⁻³. We further show that different approaches to representing updraft velocity lead to similar results: using a simple Gaussian probability density function (PDF) yields CDNC_max values about 15% lower than those obtained with an updraft-weighted PDF. Accounting for size-segregated aerosol composition reduces CDNC_max by 4–21% while increasing SS_max by 1–12%, depending on the specific updraft representation method employed for simulations. Simulations of 70 cumulus cloud cases observed between May and October in 2018 and 2019, characterized by stronger median positive updrafts (1.34 m s⁻¹) compared to the long-term median(0.36 m s⁻¹), produced median values of SS_max = 0.32% and CDNC_max = 542 cm⁻³. Sensitivity analyses, conducted using multiple approaches, revealed that updraft velocity and aerosol number concentration are the dominant controls on simulated cloud properties. However, chemical composition can become an important control under conditions with a large number of Aitken mode particles with a small mode diameter and low accumulation mode concentration.

Air pollution and fog are closely connected, influencing both visibility and human health. As relative humidity rises, aerosol particles absorb water and grow hygroscopically, potentially activating into fog droplets when supersaturation is reached. However, distinguishing between hydrated (non-activated) aerosols and activated droplets is critical, as their differing thermodynamic states influence fog chemistry and dissipation. This study quantifies the impact of hydrated aerosol particles on fog microphysical properties and visibility in the Po Valley, one of Europe’s most polluted regions. We analyzed detailed aerosol–fog observations from the 2021/22 FAIRARI campaign at San Pietro Capofiume, Italy, using κ-Köhler theory and the Large Eddy Simulation (LES) model MIMICA. The median hygroscopicity κ-value of fog residuals (0.45) exceeded that of interstitial particles (0.40) and out-of-fog aerosols (0.34), reflecting enhanced inorganic content in fog droplets. Hygroscopic growth calculations show that hydrated particles can reach several micrometers in diameter, significantly influencing inferred fog microphysical properties. Excluding hydrated aerosols led to an 81 % increase in effective diameter (from 11.6 μm to 21.0 μm) and an 87 % decrease in cloud droplet number concentration (from 97.4 to 12.4 cm-3). Hydrated particles contributed on average 21 % to liquid water content and accounted for 36 % of sub-kilometer visibility events without droplet activation. LES results emphasize that fog prediction depends strongly on the largest dry aerosol particles. Our findings demonstrate the need to distinguish between hydrated and activated particles when interpreting fog observations and modeling fog development in polluted environments.