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Open research questions in Atmospheric aerosols and clouds

82 unresolved questions extracted from the limitations and future-work sections of 561 Atmospheric aerosols and clouds papers in our library. Each links back to the study that raised it.

What the literature leaves open

  • The Taklimakan Desert (TD) is a major dust-source region in East Asia, yet the dynamic and thermal processes associated with dust-storm development remain insufficiently understood from vertically resolved observations.

    Dynamic and thermal analysis of dust storm processes based on vertical observation data · 2026 · DOI

  • It supports research on aerosol-cryosphere feedbacks, air quality, and hydroclimate over a region where observations are sparse, and its resolution and 17-year length make it suitable as a training set for statistical and machine-learning models.

    MATCHA, a novel regional hydroclimate-chemical reanalysis: System description and evaluation · 2026 · DOI

  • Although various Atmospheric Anti-/De-Icing (AADI) techniques exist, each with specific strengths and limitations, their practical application is often limited by the absence of a structured method for prioritization.

    A structured framework for prioritizing atmospheric icing mitigation techniques in critical infrastructures · 2026 · DOI

  • A core cautionary finding of this study is that, even with more than 500 co-located, high-resolution variables at one of the world's most heavily instrumented atmospheric stations, strong, deterministic links between INP concentrations and monitored parameters remain elusive.

    Exploring ice nucleation particle concentrations in a boreal environment: limits of machine-learning-assisted variable screening · 2026 · DOI

  • An aspect not explored in this study is the relationship be- tween aerosol extinction coefficient and aerosol concentra- tion and how it would impact the ACI calculations.

    Advancing the quantification of aerosol-cloud interactions with the CALIPSO-CloudSat-Aqua/MODIS record · 2026 · DOI

  • 7) may still apply for β = 1, although further work is needed to deter- mine if this is indeed the case. : Less subjective metrics for cloud shape 6967 Appendix C: Parameters for a wider range of reflectance thresholds In the main text, reflectance thresholds R used to define cloud were limited to a range between R = 0.

    Toward less subjective metrics for quantifying the shape and organization of clouds · 2026 · DOI

  • This study points to the importance of the vegetation, in par- ticular grassland, on modulating the dust cycle. By incor- porating the vegetation data simulated by the ORCHIDEE land surface model into the LMDzORINCA dust emission scheme, we quantified the variations of emissions, DAOD, concentrations, and deposition due to the sheltering effects of vegetation. Building on our recent work (Xu et al., 2026), incorporating dynamic grassland densities provides an effec- tive and feasible way to explicitly represent bare soil gaps within grasslands – a key control on dust emission. This allows for consistent estimates of vegetation characteristics from the ORCHIDEE model rather than getting information from external datasets (Foroutan et al., 2017; Klose et al., 2021; Leung et al., 2023). This study opens the way to fully couple vegetation to the dust cycle within the IPSL Earth System Model (Boucher et al., 2020). The next studies will hence focus on the feedbacks occurring in semi-arid regions between vegetation and dust cycle. It also opens the way to explore the role played by these interactions under climate change, extreme weather events, and potential tipping points. The incorporation of vegetation reduces global dust emis- sions by 23 %, with higher reductions of 35 %–78 % in semi- arid regions, exhibiting the significant positive relationships between vegetation fraction and relative emission reduction across different regions. This modification alters the regional contribution of emissions to the global dust cycle: higher contributions take place from major dust source regions with sparse vegetation and lower contributions occur from regions where vegetation cover is more abundant. The suppressive effect of vegetation also reduces significantly global dust loads from the control simulation, both in source regions – such as the Thar Desert – and in remote downwind areas, in- cluding Antarctica, where total deposition is considerably re- duced. Overall, evaluations against DAOD, surface concen- tration, and deposition observations show model improve- ments that vary from modest to significant depending on the region. This study also investigates the impact of simulated dust particle size representations. The 4-mode representation pro- vides a more comprehensive description of the dust particle size distribution, whereas the 1-mode representation, which is more computationally efficient, treats mainly micrometre- sized particles below 7 µm. The 1-mode distribution appears to adequately represent optical properties but provides a sim- plified representation of concentrations and deposition distri- butions. Models still struggle in their current state to repre- sent coarse particles. This is manifest in the long-range trans- port, in overly rapid dry deposition, and in insufficient ver- tical lofting, which all contribute to the underestimation of surface concentrations and deposition fluxes in remote down- wind regions. Taken together, these results demonstrate that accounting for vegetation effects and adopting an appropri- ate size-distribution framework are crucial for improving the physical consistency of dust simulations. Building on these findings, we identify several priori- ties for future development: (1) Improving the representa- tion of dust source restriction (e.g., temperature and pre- cipitation thresholds) and surface roughness, including the distinct aerodynamic effects of different vegetation types, non-vegetative roughness elements (e.g., rocks and pebbles), and the seasonal variation of vegetation; (2) Moving toward higher spatial resolutions to better resolve sub-grid meteoro- logical variability and orographic heterogeneity, supporting both more accurate simulation of dust processes and more robust evaluations against point-scale observational sites; (3) Improving the treatment of coarse particle dynamics in trans- port and deposition schemes, particularly gravitational set- tling and scavenging processes, to more accurately represent atmospheric residence times and long-range transport; and (4) Advancing fully coupled Earth System Model (ESM) frameworks that integrate climate, aerosols, and dynamic vegetation, enabling a more consistent representation of the bidirectional feedbacks between land-surface changes and the global dust cycle.

    Vegetation drag partition effects redistribute dust globally · 2026 · DOI

  • As CMIP transitions to the seventh iteration with updated ESMs and revised emission trajectories, the G6-1.5K-MCB scenario design choices must be evaluated and potentially optimized for compatibility with next-generation Earth system models to define the G7-1.5K-MCB scenario.

    G6-1.5K-MCB: Marine Cloud Brightening scenario design for the Geoengineering Model Intercomparison Project (GeoMIP) in CESM2.1, E3SMv2.0, and UKESM1.1 · 2026 · DOI

  • The mechanism by which ocean heat capacity integration dampens interannual MCB emission variability despite high proportional-term weighting in the controller (higher kp/ki ratios in MCB vs. SAI simulations) requires further investigation to understand ocean forcing integration timescales.

    G6-1.5K-MCB: Marine Cloud Brightening scenario design for the Geoengineering Model Intercomparison Project (GeoMIP) in CESM2.1, E3SMv2.0, and UKESM1.1 · 2026 · DOI

  • Regional MCB deployment scenarios (distinct from the global midlatitude pattern tested here) require dedicated multi-model ensemble simulations to establish their climate impacts and serve as reference points for exploratory analyses of local heat mitigation strategies.

    G6-1.5K-MCB: Marine Cloud Brightening scenario design for the Geoengineering Model Intercomparison Project (GeoMIP) in CESM2.1, E3SMv2.0, and UKESM1.1 · 2026 · DOI

  • Significant inter-model differences exist in temperature response patterns to SSP2-4.5 and midlatitude MCB across CESM2.1, E3SMv2.0, and UKESM1.1, but the underlying causes of these inter-model differences in MCB climate response sensitivity remain unexplained and require systematic investigation.

    G6-1.5K-MCB: Marine Cloud Brightening scenario design for the Geoengineering Model Intercomparison Project (GeoMIP) in CESM2.1, E3SMv2.0, and UKESM1.1 · 2026 · DOI

  • The current G6-1.5K-MCB controller uses only global annual average temperature as a single target metric, resulting in regional over- and under-cooling patterns. Development and testing of more complex controller simulations with multiple regional targets and independently adjustable iSSA emissions by geographic region is needed to address inhomogeneous climate warming responses.

    G6-1.5K-MCB: Marine Cloud Brightening scenario design for the Geoengineering Model Intercomparison Project (GeoMIP) in CESM2.1, E3SMv2.0, and UKESM1.1 · 2026 · DOI

  • A more thorough comparison is required to understand potential differences between MCB intervention methods (regional deployment vs. weather modification approaches) and their distinct regional and global climate impacts, particularly for localized applications like the Great Barrier Reef project versus global midlatitude MCB strategies.

    G6-1.5K-MCB: Marine Cloud Brightening scenario design for the Geoengineering Model Intercomparison Project (GeoMIP) in CESM2.1, E3SMv2.0, and UKESM1.1 · 2026 · DOI

  • While the study demonstrates that considering both CCN and IN effects improves precipitation simulations by approximately 40% compared to CCN-alone schemes, the underlying mechanisms responsible for this improvement remain incompletely characterized. Investigation into the relative contributions and interactions between CCN and IN pathways across different meteorological and aerosol conditions is needed.

    The impact of aerosol-ice nuclei-cloud interactions on a typical spring dust-precipitation event in China · 2026 · DOI

  • The paper identifies scarcity of real-time observations as a critical barrier to exploring detailed microphysical processes and mechanisms governing CCN and IN effects on cloud formation. Future work requires expansion of observational networks capable of measuring ice nucleating particles, cloud condensation nuclei, and their size-resolved characteristics during dust events.

    The impact of aerosol-ice nuclei-cloud interactions on a typical spring dust-precipitation event in China · 2026 · DOI

  • The study evaluated aerosol-IN-cloud interactions for only a single spring dust-precipitation event in China. Multiple case studies across different seasons (summer, autumn, winter) and varying dust concentration levels are needed to validate the generalizability of the online aerosol-IN nucleation scheme and its precipitation simulation improvements.

    The impact of aerosol-ice nuclei-cloud interactions on a typical spring dust-precipitation event in China · 2026 · DOI

  • The study demonstrates the model's performance on four case studies over the Iberian Peninsula with specific extreme events (transoceanic smoke transport, Saharan dust with PM2.5 reaching 700 µg m−3), but does not evaluate the machine learning prescription approach under background aerosol conditions or moderate pollution episodes. Statistical characterization of model accuracy across the full range of aerosol optical depth conditions and cluster occurrence frequencies is needed to assess systematic biases beyond high-impact events.

    Machine learning-driven characterization and prescription of aerosol optical properties for atmospheric models · 2026 · DOI

  • The C3 optical regime is hypothesized to represent high local or regional pollution rather than large-scale dust or smoke transport, based on its spatial distribution in eastern Iberian Peninsula throughout the year. However, this regime lacks explicit validation against ground-based measurements or chemical speciation data; the specific aerosol compositions and sources defining C3 remain unconfirmed and require targeted observational campaigns or source apportionment analysis.

    Machine learning-driven characterization and prescription of aerosol optical properties for atmospheric models · 2026 · DOI

  • The random distribution of C2 cluster patches within larger C4 smoke plumes (Case 02) is attributed to the model's response to aerosol-type column mass densities, but the paper explicitly states there is 'insufficient evidence to draw definitive conclusions' about whether these patches represent high-absorbing aerosol-type concentrations within mixed plumes. Dedicated sensitivity analyses varying aerosol-type mass density inputs and spatial resolution are required to determine the physical drivers of this spatial heterogeneity.

    Machine learning-driven characterization and prescription of aerosol optical properties for atmospheric models · 2026 · DOI

  • The C2 optical regime's association with high-absorbing fresh smoke aerosols is inferred from elemental carbon to organic carbon ratios and Brown Carbon (BrC) absorption theory, but the model prescribes C2 based only on aerosol-type columnar mass density inputs without explicit constraints on chemical composition or aging state. Direct measurement of BrC absorption evolution and elemental/organic carbon ratios during atmospheric processing within the Iberian Peninsula domain is needed to validate the optical regime assignments.

    Machine learning-driven characterization and prescription of aerosol optical properties for atmospheric models · 2026 · DOI

  • The machine learning model's Single Scattering Albedo (SSA) prescription shows systematic discrepancies with MERRA-2 reanalysis across different aerosol regimes, particularly for smoke scenarios where the model prescribed SSA at 550 nm of ~0.95 compared to MERRA-2's ~0.86–0.90. However, validation is limited by the absence of AERONET sites in northern Portugal during the August 2016 wildfire event, preventing direct comparison of prescribed versus observed SSA values for fresh, highly-absorbing smoke plumes.

    Machine learning-driven characterization and prescription of aerosol optical properties for atmospheric models · 2026 · DOI

  • To overcome these challenges, future studies should focus on an ensem- ble of trajectories based on realistic conditions in the present and warmer world to assess the impact of climate change on Atmos. The simulations carried out here are based on an ideal- ized SCT case study built from a 2-year (summer-time) com- posite trajectory that does not account for all the real-world complexity and variations (e.

    Aerosol–cloud interactions in marine low-clouds in a warmer climate · 2026 · DOI

  • This study is based on highly idealized simulations, which were designed to isolate the role of emissions spatial heterogeneity under controlled meteorological conditions. While this allows for a clear attribution of observed effects to aerosol processes such as coagulation and gas-particle partitioning, it also introduces limitations and opportunities for future investigation. Simulations in this study assume a horizontally homogeneous land surface, no synoptic-scale forcing, and a fixed solar heating profile, which neglects the potential influence of surface heterogeneity, wind shear, and diurnal variability. Additionally, all emissions – both gas-phase and particulate – are temporally constant after spin-up and spatially collocated, reflecting a deliberate idealization used here to isolate the effects of emissions spatial heterogeneity. This configuration may not fully capture the complexity of real urban emission patterns where different sources (e.g., traffic, industry, biomass burning) are spatially and temporally decoupled. Future studies should investigate the impact of emissions heterogeneity on the aerosol state in response to realistic emission patterns such as numerous point sources with spatially segregated reactive species. Emission flux observations and inventories are not uniformly available at 100 m resolution. While many gridded inventories used in regional and global-scale modeling are too coarse for direct use in LES, several important source types can be represented accurately at sub-kilometer scales. Point sources (e.g., power plants, industrial stacks) and line sources (e.g., road networks) are often well constrained spatially and can be implemented directly in high-resolution simulations. In contrast, diffuse area sources that are typically reported at county or regional scales require additional emission preprocessing, downscaling, or data-fusion approaches to distribute fluxes at finer resolution. Developing such preprocessing workflows represents an important step toward applying WRF-PartMC- LES in observationally constrained, realistic settings. Furthermore, while the chosen aerosol composition is grounded in past urban measurements (SCAQS), the limited Figure 14. Column-integrated percent difference in ammonium (left), nitrate (center), and sulfate (right) between z ∼ 800 and z ∼ 1400 m and t = 6 h, relative to the no heterogeneity, low RH scenario (top left of each subplot). For nitrate, quoted percent difference values are equal to (% difference)/1000 due to the trace amount of nitrate present in the no heterogeneity, low RH scenario. the co-condensation of semivolatile compounds and water vapor at higher RH. To address these limitations, we conducted two additional simulations for the no heterogeneity and high heterogeneity scenarios in which the domain was initialized with a moist boundary layer.

    Idealized particle-resolved large-eddy simulations to evaluate the impact of emissions spatial heterogeneity on CCN activity · 2026 · DOI

  • The sensitivity analysis for poleward moisture flux (Appendix J) across different sectoral contours (North Atlantic, Siberian, Pacific, Canadian Archipelago) suggests regional variations, but the implications and mechanisms underlying these sectoral differences are not fully explored.

    Understanding the spring cloud onset over the Arctic sea-ice · 2026 · DOI

  • The analysis relies solely on ERA5 outputs and framework from Serretz et al. (2007) to compute moist static convergence flux and net top-of-atmosphere shortwave radiation budget contributions, limiting the methodology to reanalysis data.

    Understanding the spring cloud onset over the Arctic sea-ice · 2026 · DOI

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82 open questions have been extracted from the limitations and future-work passages of 561 Atmospheric aerosols and clouds papers in our library. Each one below links back to the study that raised it, so you can read the original claim in context.

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