Martina Klose

Mineral dust is a major contributor to atmospheric aerosol mass and plays a complex role in Earth's radiation budget. However, large uncertainties remain in quantifying its direct radiative effects, partly because dust absorption is poorly constrained and in situ measurements of freshly emitted dust remain scarce. Here, we present high temporal resolution measurements of dust optical properties collected during active dust emission events in the Moroccan Sahara in September 2019. During dust emission periods, the optical properties aligned with previous Saharan dust studies. Single scattering albedo (SSA) for PM _2.5 (PM ₁₀ ) was 0.95 (0.94) at 370 nm and 0.97 (0.96) at 660 nm. Coarse particles contributed to negative scattering and SSA Ångström exponents (SSAAE), and absorption Ångström exponents (AAE) reached up to 2.5 (2.0), consistent with stronger wavelength-dependent absorption by iron oxides. The asymmetry parameter ( g ) was 0.7 (0.65) at 520 nm, and the backscatter fraction (BF) was 0.11 (0.13), showing coarse dust's impact on scattering. Mass absorption efficiency (MAE) decreased from 0.30 to 0.15 m ² g ⁻¹ at 370 nm with increasing particle size. Mass scattering efficiency (MSE) shifted towards longer wavelengths for larger particles. We further retrieved the imaginary part of the complex refractive index, k , for the PM _2.5 dust-event period under prescribed assumptions for particle shape, particle density, and the real part of the refractive index. For the central density assumption, retrieved k at 520 nm ranged from 0.00104 for spheres to 0.00145 for triaxial ellipsoids, corresponding to a 40 % increase. These results show that particle-shape assumptions can substantially affect retrieved k , and therefore need to be considered when interpreting in situ, remote-sensing, and model estimates of dust optical properties.

Haboobs, dust storms generated by the cold pool outflow from moist convection, are a major dust uplift mechanism, especially over northern Africa in boreal summer. However, systematic model investigations of haboob properties and their contribution to total dust emissions remain limited. Such studies require not only computationally expensive convection-permitting models with an online dust cycle to explicitly simulate haboobs and their associated dust emissions, but also a reliable method to detect haboobs in model output. Here we develop HITRA (Haboob Identification, Tracking and Dust-emission Attribution Algorithm) v1.0, an automated algorithm to detect and characterize haboobs. We apply HITRA to a one-year simulation for 2011 with the Multiscale Online Non-hydrostatic AtmospheRe CHemistry model (MONARCH) including online dust at approximately 3 km resolution over North Africa and the Middle East. This allows us to assess both the algorithm performance and the realism of the simulated haboob characteristics. After filtering short-lived events and coastal and orographic artefacts, the diagnostic identified 3,371 haboob events during the simulation year. The resulting event catalogue shows that MONARCH captures key haboob features, including Sahelian hotspots, boreal-summer seasonality, afternoon-to-evening occurrence, mesoscale sizes, and lifetimes of a few hours. Haboobs account for 6.65 % of annual domain-total dust emission and 9.72 % in JJA, with local contributions exceeding 50 % in parts of the Sahel. These results demonstrate that convection-permitting modeling is able to represent haboobs overall realistically and HITRA provides a systematic framework for systematically identifying and characterizing haboobs and quantifying their contribution to dust emissions in convection-permitting simulations.

Proglacial valleys of western Canada and Alaska demonstrate extensive historical and contemporary records of mineral dust emissions. These contributions remain unresolved by current dust emission modelling and unaccounted for in global emission estimates. We developed and evaluated a sub-km implementation of the Weather Research and Forecasting model with Chemistry (WRF-Chem) capable of simulating dust emissions from proglacial valleys of the St. Elias Mountains, Canada. Modelling these dust sources required precise treatment of surface characteristics and wind dynamics to accurately resolve surface erodibility, emission rates and aerosol dispersion within this mountainous terrain. Land-surface inputs were overhauled, with explicit treatment of glaciofluvial deposit heterogeneity and inundation conditions. Simulations covering 5–19 d periods across 2019–2022 were evaluated against in situ meteorological and dust emission measurements, camera stations and surface-based Doppler LiDAR data. A total emission rate of 1.0×10 ⁴ kg km ⁻² d ⁻¹ was estimated from erodible deposits across 47 d of simulation, providing a first-order estimate of emissions from these valleys during dusty periods. Seasonal-dependent skill in reproducing surface meteorology and in-valley vertical dispersion is demonstrated, modifying dust dispersion. Emission dynamics from a variety of glaciofluvial deposits were successfully reproduced, however the sensitivity of the emission scheme to soil texture is discussed in light of glaciofluvial deposit heterogeneity and dataset scarcity. The successful model implementation under extreme topographic conditions and arguably the most severely constrained deposits (channel width: 0.1–3 km; sidewalls up to +1.7 km) supports the potential of this approach to simulate dust emissions from currently unaccounted for proglacial valleys across northwest North America and other regions.

Proglacial valleys of western Canada and Alaska demonstrate extensive historical and contemporary records of mineral dust emissions. These contributions remain unresolved by current dust emission modelling and unaccounted for in global emission estimates. We developed and evaluated a sub-km implementation of the Weather Research and Forecasting model with Chemistry (WRF-Chem) capable of simulating dust emissions from proglacial valleys of the St. Elias Mountains, Canada. Modelling these dust sources required precise treatment of surface characteristics and wind dynamics to accurately resolve surface erodibility, emission rates and aerosol dispersion within this mountainous terrain. Land-surface inputs were overhauled, with explicit treatment of glaciofluvial deposit heterogeneity and inundation conditions. Simulations covering 5–19 day periods across 2019–2022 were evaluated against in situ meteorological and dust emission measurements, camera stations and surface-based Doppler LiDAR data. A total emission rate of 1.0 × 10⁴ kg km^–2 day^–1 was estimated from erodible deposits across 47 days of simulation. Seasonal-dependent skill in reproducing surface meteorology and in-valley vertical dispersion is demonstrated, modifying dust dispersion. Emission dynamics from a variety of glaciofluvial deposits were successfully reproduced, however the sensitivity of the emission scheme to soil texture is discussed in light of glaciofluvial deposit heterogeneity and dataset scarcity. The successful model implementation under extreme topographic conditions and arguably the most severely constrained deposits (channel width: 0.1 – 3 km; sidewalls up to +1.7 km) supports the potential of this approach to simulate dust emissions from currently unaccounted for proglacial valleys across northwest North America and other regions.

Ice nucleation activity (INA) in the mixed-phase cloud (MPC) regime has been extensively studied. Nevertheless, most research has focused on particles smaller than a few micrometers, leaving the INA dependence on mineralogical composition and size poorly characterized for particles larger than ten micrometers in diameter. This gap is important because: (1) large mineral dust particles (LMDPs) can undergo long-range atmospheric transport and reach altitudes where MPCs form; (2) their mineralogical composition may differ from that of smaller particles, affecting the density of ice-active sites; and (3) larger particles are more susceptible to atmospheric aging through coatings by chemical and organic compounds, which can further modify their INA. Here, we used natural soil dust samples collected during field campaigns in Morocco and Iceland, covering particle sizes from fine to super-coarse dust and characterized in terms of mineralogical composition and size. The samples' INA was measured in the AIDA and AIDAm cloud chambers and the INSEKT and IR-DROFA freezing assays in more than 300 experiments. Moroccan samples exhibited INA comparable to that reported for K-feldspar in previous studies, with no dependence on particle size, consistent with the nearly constant K-feldspar fraction across all size bins. In contrast, Icelandic samples showed lower INA than in other studies using samples of similar composition, along with a subtle size dependence linked to the size-varying pyroxene content, which decreased with increasing particle size. Our findings elucidate the role of LMDPs in immersion freezing and their relationship with mineralogy and size for low- and high-latitude dust sources.

Proglacial valleys of western Canada and Alaska demonstrate extensive historical and contemporary records of mineral dust emissions. These contributions remain unresolved by current dust emission modelling and unaccounted for in global emission estimates. We developed and evaluated a sub-km implementation of the Weather Research and Forecasting model with Chemistry (WRF-Chem) capable of simulating dust emissions from proglacial valleys of the St. Elias Mountains, Canada. Modelling these dust sources required precise treatment of surface characteristics and wind dynamics to accurately resolve surface erodibility, emission rates and aerosol dispersion within this mountainous terrain. Land-surface inputs were overhauled, with explicit treatment of glaciofluvial deposit heterogeneity and inundation conditions. Simulations covering 5–19 day periods across 2019–2022 were evaluated against in situ meteorological and dust emission measurements, camera stations and surface-based Doppler LiDAR data. A total emission rate of 1.0 × 10⁴ kg km^–2 day^–1 was estimated from erodible deposits across 47 days of simulation. Seasonal-dependent skill in reproducing surface meteorology and in-valley vertical dispersion is demonstrated, modifying dust dispersion. Emission dynamics from a variety of glaciofluvial deposits were successfully reproduced, however the sensitivity of the emission scheme to soil texture is discussed in light of glaciofluvial deposit heterogeneity and dataset scarcity. The successful model implementation under extreme topographic conditions and arguably the most severely constrained deposits (channel width: 0.1 – 3 km; sidewalls up to +1.7 km) supports the potential of this approach to simulate dust emissions from currently unaccounted for proglacial valleys across northwest North America and other regions.

Proglacial valleys of western Canada and Alaska demonstrate extensive historical and contemporary records of mineral dust emissions. These contributions remain unresolved by current dust emission modelling and unaccounted for in global emission estimates. We developed and evaluated a sub-km implementation of the Weather Research and Forecasting model with Chemistry (WRF-Chem) capable of simulating dust emissions from proglacial valleys of the St. Elias Mountains, Canada. Modelling these dust sources required precise treatment of surface characteristics and wind dynamics to accurately resolve surface erodibility, emission rates and aerosol dispersion within this mountainous terrain. Land-surface inputs were overhauled, with explicit treatment of glaciofluvial deposit heterogeneity and inundation conditions. Simulations covering 5–19 day periods across 2019–2022 were evaluated against in situ meteorological and dust emission measurements, camera stations and surface-based Doppler LiDAR data. A total emission rate of 1.0 × 10⁴ kg km^–2 day^–1 was estimated from erodible deposits across 47 days of simulation. Seasonal-dependent skill in reproducing surface meteorology and in-valley vertical dispersion is demonstrated, modifying dust dispersion. Emission dynamics from a variety of glaciofluvial deposits were successfully reproduced, however the sensitivity of the emission scheme to soil texture is discussed in light of glaciofluvial deposit heterogeneity and dataset scarcity. The successful model implementation under extreme topographic conditions and arguably the most severely constrained deposits (channel width: 0.1 – 3 km; sidewalls up to +1.7 km) supports the potential of this approach to simulate dust emissions from currently unaccounted for proglacial valleys across northwest North America and other regions.

Proglacial valleys of western Canada and Alaska demonstrate extensive historical and contemporary records of mineral dust emissions. These contributions remain unresolved by current dust emission modelling and unaccounted for in global emission estimates. We developed and evaluated a sub-km implementation of the Weather Research and Forecasting model with Chemistry (WRF-Chem) capable of simulating dust emissions from proglacial valleys of the St. Elias Mountains, Canada. Modelling these dust sources required precise treatment of surface characteristics and wind dynamics to accurately resolve surface erodibility, emission rates and aerosol dispersion within this mountainous terrain. Land-surface inputs were overhauled, with explicit treatment of glaciofluvial deposit heterogeneity and inundation conditions. Simulations covering 5–19 day periods across 2019–2022 were evaluated against in situ meteorological and dust emission measurements, camera stations and surface-based Doppler LiDAR data. A total emission rate of 1.0 × 10⁴ kg km^–2 day^–1 was estimated from erodible deposits across 47 days of simulation. Seasonal-dependent skill in reproducing surface meteorology and in-valley vertical dispersion is demonstrated, modifying dust dispersion. Emission dynamics from a variety of glaciofluvial deposits were successfully reproduced, however the sensitivity of the emission scheme to soil texture is discussed in light of glaciofluvial deposit heterogeneity and dataset scarcity. The successful model implementation under extreme topographic conditions and arguably the most severely constrained deposits (channel width: 0.1 – 3 km; sidewalls up to +1.7 km) supports the potential of this approach to simulate dust emissions from currently unaccounted for proglacial valleys across northwest North America and other regions.

Ice nucleating particles (INPs) are aerosols that lower the energy barrier for ice formation in mixed phase clouds, and therefore impact the liquid water fraction in these clouds. Hence, the prevalence of INPs affects the radiative properties of the cloud, as well as the precipitation formation. In addition, the liquid water fraction of clouds is one factor thought to be contributing to the consistent Southern Ocean radiation bias found across CMIP6 models. Cloud-aerosol interactions remain a major area of uncertainty in climate modelling, and non-aerosol-aware models use INP parameterisations that are purely temperature dependent and do not take into account the regional variation in aerosol concentration and type. Here, we will present modelled global distributions of mineral dust and marine organic INPs – and their relative contributions to the total INP – calculated from simulations with the Aerosol and Reactive Trace (ART) gases module of the ICOsahedral Nonhydrostatic NWP model (ICON). The INPs are calculated offline using the Ice-nucleating active site (INAS) densities for immersion freezing provided by Ullrich et al 2014 for dust INP and McCluskey et al 2018 for marine INP. The simulations run for one year at 80km horizontal grid spacing. A comparison to the temperature-only parameterisations and observations of INPs is made, with a particular focus on Antarctica (data from Wex et al 2025) and the Southern Ocean (Antarctic Circumnavigation Expedition ship campaign).Furthermore, we calculate INPs using a preliminary version of the seamless ICON-ART (ICON-SmART) model and evaluate this. Ultimately, the aim is to improve online INP modelling in ICON-SmART, in part to address the Southern Ocean radiation bias found in seasonal to decadal simulations.