Elisabeth Andrews

Black carbon (BC) aerosol is an important driver of Arctic warming, and China used to be a major contributor to the Arctic BC burden through long‐range atmospheric transport. Here we show that China's contribution declined significantly from 2009 to 2022, primarily due to reductions in domestic BC emissions following the implementation of clean air policies. Global chemistry‐transport model simulations indicate a relative decline of ∼3% yr ⁻¹ ( p < 0.05) in China's Arctic BC contribution, exceeding the decrease rate in the underlying emission inventory. Sensitivity simulations further suggest that climate change‐induced shifts in atmospheric transport may have amplified this decline. Observations of aerosol absorption coefficients ( σ _ap ) at Arctic background observatories revealed steeper declines in σ _ap when modeled China‐to‐total BC ratios were higher. Moreover, σ _ap correlated positively with modeled BC from China, with stronger relationships as the modeled China‐to‐total BC ratios increased. Together, these results provide robust evidence that China's emission reductions have diminished its role in Arctic BC, contributing an estimated 0.02 W m ⁻² decrease in the direct radiative effect during the Arctic haze season.

Aerosol particles resulting from both wildfires and dust events introduce considerable uncertainty into both climate research and public health assessments. These challenges are becoming particularly evident in the western US. To gain a deeper understanding of western US aerosol properties, we analyzed 13 years (2011–2024) of surface in-situ aerosol optical data from Storm Peak Laboratory (SPL) in northwestern Colorado, and 6 years (2019–2024) of surface in-situ aerosol optical data from Table Mountain (BOS) in central Colorado. The aerosol optical properties at both sites demonstrate a strong summer wildfire smoke signal (peaking in August) and evidence of springtime dust events. BOS exhibited higher aerosol loading than SPL, particularly during spring and winter, consistent with the proximity of BOS to urban sources and its lower elevation. While the general patterns observed for SPL are consistent with a previous climatological analysis (covering the period 2011–2016) for the site, the longer SPL dataset used here shows that there has been a significant increase in extreme wildfire smoke events for 2017–2024 relative to 2011–2016. Both summer and fall exhibit statistically significant positive trends in the upper percentiles of scattering coefficient with trends of 10 ± 1 % yr⁻¹ at the 98th percentile in the summer and 2.4 ± 0.4 % yr⁻¹ at the 96th percentile in the fall. Co-variability among some of the aerosol optical properties is used to further identify aerosol types and temporal patterns, demonstrating similarities between the two sites.

This study presents a comprehensive phenomenological analysis of cloud condensation nuclei (CCN) and aerosol properties – including activation properties, microphysical characteristics, chemical composition, and optical properties – across nine surface sites in different environments. Aerosol properties vary widely, reflecting the diverse environments, and controlling the CCN activation characteristics. Despite their critical role in aerosol–cloud interactions, CCN observations remain sparse and unevenly distributed, limiting global assessments of activation behavior. To address this gap, this study presents CCN predictive methods based on chemical composition combined with particle number size distribution (PNSD) data, and aerosol optical properties (AOPs). The chemical composition driven predictions are tested using three hygroscopicity schemes. All schemes overpredict the CCN concentrations (median relative bias; MRB = 13 %–15 %), although the two composition-derived CCN concentrations are markedly better predictors than the fixed-κ_chem assumption (MRB = 24 %). The AOPs-derived CCN prediction is based on two approaches: first, an extended empirical parameterization of Shen et al. (2019) (hereafter S2019) to 13 stations, which reduces bias from −27 % to −8 % and improves CCN agreement; and second, a random forest model that infers Twomey activation parameters (C and k) using both the S2019 variables and all the available AOPs. Including all AOPs reduces MRB from 19 % to 15 % and highlights the role of absorption in predicting CCN activation. These findings demonstrate that both chemical and optical measurements can provide a reasonable estimate of CCN concentrations when direct measurements are unavailable. These results will enable retrospective analyses of long-term aerosol time series to investigate aerosol–cloud interactions.

Harmonized observations of aerosols, clouds and trace gases with global coverage are essential for advancing weather prediction, climate science and air quality research. They support model development and simulation and enable the calibration and validation of current and future satellite missions. While major ground-based research infrastructures (RIs) and observational networks have been developed in Europe and the United States (US) with long-term perspectives [1], their global integration remains fragmented. This is primarily due to differences in governance and access mechanisms, and to a lesser extent to operational practices and data policies. The Horizon Europe project CARGO-ACT [2] addresses these challenges by developing a roadmap for sustainable global cooperation among key atmospheric RIs, with the long-term vision of building a sustainable and coherent international framework.As proof of concept, CARGO-ACT evaluated the consistency and compatibility of data, operations, governance and access mechanisms for aerosol in-situ and remote sensing networks in the US (DOE/ARM, NASA/MPLNET, NOAA/GML, ASCENT) [3] and Europe (ACTRIS [4]). By bringing together network leaders and leading experts, the project promotes convergence towards interoperability and FAIR principles between ACTRIS and its US counterparts through a common data management framework and aligned data policies. Scientific robustness and comparability are addressed through the development of harmonised operating procedures, calibration strategies, and data quality methodologies, providing a solid basis for mutual trust, reproducibility and long-term sustainability of global observations.As a concrete demonstration of data interoperability, the US Department of Energy’s Atmospheric Radiation Measurement (ARM) program enabled bi-directional metadata exchange by harvesting and indexing metadata from CARGO-ACT participants (such as ACTRIS) within the ARM data portal, and by providing ARM metadata APIs to support discovery and reuse of relevant ARM datasets by the ACTRIS data portal. Another outcome of the project is the ongoing revision of the WMO/GAW report on in-situ measurements [5], led by CARGO-ACT participants. In tandem with the technical aspects, CARGO-ACT proposes strategies for coordinating governance and aligning global objectives through structured stakeholder engagement and mechanisms to support cooperation across diverse scientific priorities. The project delivers strategic recommendations for sustainable international access to global atmospheric RIs, advocating policy alignment, legal and organisational flexibility, sustainable financial and operational models, and effective coordination platforms. The CARGO-ACT approach is applicable across multiple measurement variables and observational networks.Overall, CARGO-ACT demonstrates that sustainable global cooperation in atmospheric research is not only technically feasible but strategically essential. In the context of global environmental challenges that extend beyond national and continental boundaries, strengthened international cooperation is a prerequisite for ensuring resilient, interoperable and globally coherent observing systems capable of supporting science, policy, and society in the long run. [1] https://doi.org/10.1175/AMSMONOGRAPHS-D-15-0045.1[2] CARGO-ACT: https://www.cargo-act.eu/[3] DOE/ARM: https://www.arm.gov/), NASA/MPLNET: https://mplnet.gsfc.nasa.gov/), NOAA/GML: ), ASCENT: https://ascent.research.gatech.edu/)[4] ACTRIS: https://www.actris.eu/[5] WMO/GAW (2016), Report 227, https://www.wmo-gaw-sag-aerosol.org/files/FINAL_GAW_227.pdf

Aerosol-cloud interactions (ACI) remain among the largest sources of uncertainty in assessing anthropogenic impacts on climate (IPCC, 2023), largely due to limited understanding of aerosol sources and their evolution into cloud condensation nuclei (CCN). Reducing this uncertainty requires improved characterization of CCN concentrations and their spatiotemporal variability.Although CCN measurements are increasingly available at ground-based station, long-term and spatially extensive datasets remain scarce. Harmonized CCN datasets such as those by Schmale et al. (2017) and Andrews et al. (2025) provide quality-assured observations across multiple stations and environments.To extend CCN information beyond direct measurements, several approaches have been proposed to predict CCN concentrations from more routinely measured aerosol properties, such as aerosol optical properties (AOPs). Using the harmonized Andrews et al. (2025) dataset, Zabala et al. (2025) developed two AOP-based approaches: (i) an empirical parameterization and (ii) a Random Forest (RF) method, based on observations from nine stations (blue in Figure 1). Both methods demonstrate significant potential to extend CCN estimates across space and time.Harmonized CCN observations have also enabled model evaluation studies. Fanourgakis et al. (2019) evaluated 14 general circulation models against CCN observations from nine stations (orange in Figure 1) over 2011–2015, showing systematic underestimation and substantial variability across environments.Figure 1. Map of the sites considered in this work.Motivated by the skill of AOP-based CCN prediction methods, this study applies the two approaches proposed by Zabala et al. (2025) to additional stations with available measurements. The predicted CCN values are evaluated against independent harmonized CCN observations from Schmale et al. (2017) and compared with multimodel CCN estimates reported by Fanourgakis et al. (2019), enabling a consistent assessment across diverse environments.As an example, Figure 2 shows monthly median CCN concentrations (NCCN) at 0.5% supersaturation (SS) for the SMEAR (SMR, 61°51'N, 24°17'E, 181 m) station in Finland, including observations, AOP-based predictions and CAM5-MAM3 model simulations. The empirical parameterization and the model generally underestimate NCCN (median relative biases of -30% and -14%), whereas the RF approach overestimates observations (MRB=75%). Both prediction approaches capture the seasonal cycle, with larger amplitude in the RF estimates. This behavior is consistent across all tested SS.Figure 2. Monthly median NCCN (SS=0.5%) at the SMR station from observations, AOP-based predictions, and the CAM5–MAM3 model; shaded area shows the interquartile model range.Overall, this work demonstrates that AOP-based CCN prediction approaches can reliably extend CCN information beyond observational gaps when evaluated across multiple environments and benchmarked against observations and models. These approaches provide a pathway to improve global CCN datasets, support model evaluation, and reduce uncertainties in ACI in climate models.This work was supported by the US Department of Energy (DE-SC0022886), the University of Granada (UCE-PP2017-02), and the NUCLEUS (PID2021-128757OB-I00) and MIXDUST (PID2024-160280NB-I00) projects funded by MICIU/AEI, EU NextGenerationEU/PRTR, and FEDER. We acknowledge EBAS (NILU) for the observational data.ReferencesAndrews et al. (2025). Sci. Data. 12, 937. Dataset.Fanourgakis et al. (2019). Atmos. Chem. Phys., 19, 8591–8617.IPCC (2023). Cambridge Uni. Press., Cambridge.Schmale et al. (2017). Sci. Data. 4, 937. 170003.Zabala et al. (2025). EGUsphere [preprint].

Aerosol particles resulting from both wildfires and dust events introduce considerable uncertainty into both climate research and public health assessments. These challenges are becoming particularly evident in the western U.S. To gain a deeper understanding of western U.S. aerosol properties, we analyzed 13 years (2011–2024) of surface in-situ aerosol optical data from Storm Peak Laboratory (SPL) in northwestern Colorado, and 6 years (2019–2024) of surface in-situ aerosol optical data from Table Mountain (BOS) in central Colorado. The aerosol optical properties at both sites demonstrate a strong summer wildfire smoke signal (peaking in August) and evidence of springtime dust events. BOS exhibited higher aerosol loading than SPL, particularly during spring and winter, consistent with the proximity of BOS to urban sources and its lower elevation. While the general patterns observed for SPL are consistent with a previous climatological analysis (covering the period 2011–2016) for the site, the longer SPL dataset used here shows that there has been a significant increase in extreme wildfire smoke events for 2017–2024 relative to 2011–2016. Both summer and fall exhibit statistically significant increasing trends in the upper percentiles of scattering coefficient with trends of 10 ± 1 % yr^-1 at the 98^th percentile in the summer and 2.4 ± 0.4 % yr^-1 at the 96^th percentile in the fall. Co-variability among some of the aerosol optical properties is used to further identify aerosol types and temporal patterns, demonstrating similarities between the two sites.

Aerosol particles resulting from both wildfires and dust events introduce considerable uncertainty into both climate research and public health assessments. These challenges are becoming particularly evident in the western U.S. To gain a deeper understanding of western U.S. aerosol properties, we analyzed 13 years (2011–2024) of surface in-situ aerosol optical data from Storm Peak Laboratory (SPL) in northwestern Colorado, and 6 years (2019–2024) of surface in-situ aerosol optical data from Table Mountain (BOS) in central Colorado. The aerosol optical properties at both sites demonstrate a strong summer wildfire smoke signal (peaking in August) and evidence of springtime dust events. BOS exhibited higher aerosol loading than SPL, particularly during spring and winter, consistent with the proximity of BOS to urban sources and its lower elevation. While the general patterns observed for SPL are consistent with a previous climatological analysis (covering the period 2011–2016) for the site, the longer SPL dataset used here shows that there has been a significant increase in extreme wildfire smoke events for 2017–2024 relative to 2011–2016. Both summer and fall exhibit statistically significant increasing trends in the upper percentiles of scattering coefficient with trends of 10 ± 1 % yr^-1 at the 98^th percentile in the summer and 2.4 ± 0.4 % yr^-1 at the 96^th percentile in the fall. Co-variability among some of the aerosol optical properties is used to further identify aerosol types and temporal patterns, demonstrating similarities between the two sites.

This study presents a comprehensive phenomenological analysis of cloud condensation nuclei (CCN) and aerosol properties — including activation properties, microphysical characteristics, chemical composition, and optical properties — across ten surface sites in different environments. Aerosol properties vary widely, reflecting the diverse environments, and controlling the CCN activation characteristics. Despite their critical role in aerosol–cloud interactions, CCN observations remain sparse and unevenly distributed, limiting global assessments of activation behavior. To address this gap, this study presents CCN predictive methods based on chemical composition combined with particle number size distribution (PNSD) data, and aerosol optical properties (AOPs). The chemical composition driven predictions are tested using three hygroscopicity schemes. All schemes overpredict the CCN concentrations (median relative bias; MRB=13–15 %), although the two composition-derived CCN concentrations are markedly better predictors than the fixed-κ_chem assumption (MRB=24 %). The AOPs-derived CCN prediction is based on two approaches: an extended empirical parameterization of Shen et al. (2019) (hereafter S2019) to 13 stations, which reduces bias from - 27 % to - 8 % and improves CCN agreement; and second, a random forest model that infers Twomey activation parameters (C and k) using both the S2019 variables and all the available AOPs. Including all AOPs reduces MRB from 19 % to 15 % and highlights the role of absorption in predicting CCN activation. These findings demonstrate that both chemical and optical measurements can provide a reasonable estimate of CCN concentrations when direct measurements are unavailable. These results enable retrospective analyses of long-term aerosol time series to investigate aerosol–cloud interactions.

Aerosol particles resulting from both wildfires and dust events introduce considerable uncertainty into both climate research and public health assessments. These challenges are becoming particularly evident in the western U.S. To gain a deeper understanding of western U.S. aerosol properties, we analyzed 13 years (2011–2024) of surface in-situ aerosol optical data from Storm Peak Laboratory (SPL) in northwestern Colorado, and 6 years (2019–2024) of surface in-situ aerosol optical data from Table Mountain (BOS) in central Colorado. The aerosol optical properties at both sites demonstrate a strong summer wildfire smoke signal (peaking in August) and evidence of springtime dust events. BOS exhibited higher aerosol loading than SPL, particularly during spring and winter, consistent with the proximity of BOS to urban sources and its lower elevation. While the general patterns observed for SPL are consistent with a previous climatological analysis (covering the period 2011–2016) for the site, the longer SPL dataset used here shows that there has been a significant increase in extreme wildfire smoke events for 2017–2024 relative to 2011–2016. Both summer and fall exhibit statistically significant increasing trends in the upper percentiles of scattering coefficient with trends of 10 ± 1 % yr^-1 at the 98^th percentile in the summer and 2.4 ± 0.4 % yr^-1 at the 96^th percentile in the fall. Co-variability among some of the aerosol optical properties is used to further identify aerosol types and temporal patterns, demonstrating similarities between the two sites.

Aerosol particles resulting from both wildfires and dust events introduce considerable uncertainty into both climate research and public health assessments. These challenges are becoming particularly evident in the western U.S. To gain a deeper understanding of western U.S. aerosol properties, we analyzed 13 years (2011–2024) of surface in-situ aerosol optical data from Storm Peak Laboratory (SPL) in northwestern Colorado, and 6 years (2019–2024) of surface in-situ aerosol optical data from Table Mountain (BOS) in central Colorado. The aerosol optical properties at both sites demonstrate a strong summer wildfire smoke signal (peaking in August) and evidence of springtime dust events. BOS exhibited higher aerosol loading than SPL, particularly during spring and winter, consistent with the proximity of BOS to urban sources and its lower elevation. While the general patterns observed for SPL are consistent with a previous climatological analysis (covering the period 2011–2016) for the site, the longer SPL dataset used here shows that there has been a significant increase in extreme wildfire smoke events for 2017–2024 relative to 2011–2016. Both summer and fall exhibit statistically significant increasing trends in the upper percentiles of scattering coefficient with trends of 10 ± 1 % yr^-1 at the 98^th percentile in the summer and 2.4 ± 0.4 % yr^-1 at the 96^th percentile in the fall. Co-variability among some of the aerosol optical properties is used to further identify aerosol types and temporal patterns, demonstrating similarities between the two sites.