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Abstract. New particle formation (NPF) events are defined as asudden burst of aerosols followed by growth and can impact climate bygrowing to larger sizes and under proper conditions, potentially formingcloud condensation nuclei (CCN). Field measurements relating NPF and CCN arecrucial in expanding regional understanding of how aerosols impactclimate. To quantify the possible impact of NPF on CCN formation, it isimportant to not only maintain consistency when classifying NPF events butalso consider the proper timeframe for particle growth to CCN-relevantsizes. Here, we analyze 15 years of direct measurements of both aerosol sizedistributions and CCN concentrations and combine them with novel methods toquantify the impact of NPF on CCN formation at Storm Peak Laboratory (SPL),a remote, mountaintop observatory in Colorado. Using the new automaticmethod to classify NPF, we find that NPF occurs on 50 % of all daysconsidered in the study from 2006 to 2021, demonstrating consistency withprevious work at SPL. NPF significantly enhances CCN during the winter by afactor of 1.36 and during the spring by a factor of 1.54, which, when combined withprevious work at SPL, suggests the enhancement of CCN by NPF occurs on aregional scale. We confirm that events with persistent growth are common inthe spring and winter, while burst events are more common in the summer andfall. A visual validation of the automatic method was performed in thestudy. For the first time, results clearly demonstrate the significantimpact of NPF on CCN in montane North American regions and the potential forwidespread impact of NPF on CCN. 

Abstract Ambient fine particulate matter (PM 2.5 ) is the world’s leading environmental health risk factor. Reducing the PM 2.5 disease burden requires specific strategies that target dominant sources across multiple spatial scales. We provide a contemporary and comprehensive evaluation of sector- and fuel-specific contributions to this disease burden across 21 regions, 204 countries, and 200 sub-national areas by integrating 24 global atmospheric chemistry-transport model sensitivity simulations, high-resolution satellite-derived PM 2.5 exposure estimates, and disease-specific concentration response relationships. Globally, 1.05 (95% Confidence Interval: 0.74–1.36) million deaths were avoidable in 2017 by eliminating fossil-fuel combustion (27.3% of the total PM 2.5 burden), with coal contributing to over half. Other dominant global sources included residential (0.74 [0.52–0.95] million deaths; 19.2%), industrial (0.45 [0.32–0.58] million deaths; 11.7%), and energy (0.39 [0.28–0.51] million deaths; 10.2%) sectors. Our results show that regions with large anthropogenic contributions generally had the highest attributable deaths, suggesting substantial health benefits from replacing traditional energy sources. 

We investigate and assess how well a global chemical transport model (GEOS‐Chem) simulates submicron aerosol mass concentrations in the remote troposphere. The simulated speciated aerosol (organic aerosol (OA), black carbon, sulfate, nitrate, and ammonium) mass concentrations are evaluated against airborne observations made during all four seasons of the NASA Atmospheric Tomography Mission (ATom) deployments over the remote Pacific and Atlantic Oceans. Such measurements over pristine environments offer fresh insights into the spatial (Northern [NH] and Southern Hemispheres [SH], Atlantic, and Pacific Oceans) and temporal (all seasons) variability in aerosol composition and lifetime, away from continental sources. The model captures the dominance of fine OA and sulfate aerosol mass concentrations in all seasons. There is a high bias across all species in the ATom‐2 (NH winter) simulations; implementing recent updates to the wet scavenging parameterization improves our simulations, eliminating the large ATom‐2 (NH winter) bias, improving the ATom‐1 (NH summer) and ATom‐3 (NH fall) simulations, but producing a model underestimate in aerosol mass concentrations for the ATom‐4 (NH spring) simulations. Following the wet scavenging updates, simulated global annual mean aerosol lifetimes vary from 1.9 to 4.0 days, depending on species. Aerosol lifetimes in each hemisphere vary by season, and are longest for carbonaceous aerosol during the southern hemispheric fire season. The updated wet scavenging parameterization brings simulated concentrations closer to observations and reduces global aerosol lifetime for all species, indicating the sensitivity of global aerosol lifetime and burden to wet removal processes.

 

Atmospheric ammonia plays an important role in a number of environmental issues, including new particle formation and aerosol indirect radiative forcing. Over the United States, atmospheric ammonia has seen an increasing trend due in most part to the declining SO₂and NO_xemissions. We conduct the first comprehensive assessment of multiyear Goddard Earth Observing System (GEOS)‐Chem simulated ammonia concentration ([NH₃]) over conterminous United States along with surface observations from all 90 National Atmospheric Deposition Program Ammonia Monitoring Network (AMoN) sites that have at least 2 years of continuous measurements. Model‐simulated [NH₃] is along empirically expected lines with regard to temporal trends, seasonal variations, and spatial distribution. GEOS‐Chem‐simulated [NH₃], compared to AMoN observed values, has weighted average correlation (τ) of 0.50 ± 0.15 and mean fractional bias (MFB) of −8.8 ± 56%. Most sites (63 out of 90) have −60% <MFB< +60%. The deviations from observed values vary spatially and seasonally, and there is significant wintertime underestimation (−44 ± 58%) across most of conterminous United States (except the Pacific states). The largest positive deviations occur in the Pacific states (101 ± 46%) and the largest negative deviations in the Southern Plain states (−73 ± 39%) and the Mountain states (−73 ± 84%), both in the winter months. Over the Great Plains region, GEOS‐Chem simulated [NH₃] shows a much stronger dependence to emissions than AMoN observed [NH₃], indicating scope for improved representation of emissions for the region. Over Southeast United States, there appears to be the strong effect of the changing emissions of SO₂and NO_xin both modeled and observed [NH₃].

 

Ice crystal habit significantly impacts ice crystal processes such as growth by vapor deposition. Despite this, most bulk microphysical models disregard this natural shape effect and assume ice to grow spherically. This paper focuses on how the evolution of ice crystal shape and choice of ice nucleation parameterization in the adaptive habit microphysics model (AHM) influence the lake-effect storm that occurred during intensive observing period 4 (IOP4) of the Ontario Winter Lake-effect Systems (OWLeS) field campaign. This localized snowstorm produced total accumulated liquid-equivalent precipitation amounts up to 17.92 mm during a 16-h time period, providing a natural laboratory to investigate the ice–liquid partitioning within the cloud, various microphysical process rates, the accumulated precipitation magnitude, and its associated spatial distribution. Two nucleation parameterizations were implemented, and aerosol data from a size-resolved advanced particle microphysics (APM) model were ingested into the AHM for use in parameterizing ice and cloud condensation nuclei. Simulations allowing ice crystals to grow nonspherically produced 1.6%–2.3% greater precipitation while altering the nucleation parameterization changed the type of accumulating hydrometeors. In addition, all simulations were highly sensitive to the domain resolution and the source of initial and boundary conditions. These findings form the foundational understanding of relationships among ice crystal habit, nucleation parameterizations, and resultant cold-season mesoscale precipitation within detailed bulk microphysical models allowing adaptive habit.

 

Shipping particle number emission is important as it can influence cloud condensation nuclei abundance and thus indirectly affect clouds and perturb the Earth's radiation budget. Here, we integrate a size‐resolved Advanced Particle Microphysics module with a photochemical BOX MOdeling eXtension to the Kinetic PreProcessor and employ the resulting model to understand the microphysical and chemical characteristics of ship plumes. Simulated concentrations of key gaseous species and particle numbers are in good agreement compared with measurements from the NOAA Intercontinental Transport and Chemical Transformation (ITCT) 2K2 field study off the California coast. Further analysis reveals that significant new particle formation can occur in the plume and the growth of these secondary particles to 5–20 nm generally dominates the total particle numbers. We show that wind speed, emission rates of SO₂and NO_x, solar irradiation, ambient temperature, and background [NH₃] have strong nonlinear effects on the ship particle number emission index (EIPN). Depending on the ambient air and meteorological conditions, the model simulations show that EIPN can range from ∼2.5 × 10¹⁴no. kg⁻¹fuel (dominated by primary particles) to ∼3.0 × 10¹⁸no. kg⁻¹fuel (dominated by secondary particles). In consideration of the current worldwide expansion of Emission Control Areas, we systematically study how the EIPN decreases with reduction of fuel sulfur content to 0.1%. Our study highlights the necessity of accounting for the nonlinear dependence of secondary particle formation on key controlling parameters in calculating shipping particle number emissions, which is important for determining aerosol indirect climate effects.

 

Cloud condensation nuclei (CCN) are mediators of aerosol‐cloud interactions, which contribute to the largest uncertainty in climate change prediction. Here, we present a machine learning (ML)/artificial intelligence (AI) model that quantifies CCN from model‐simulated aerosol composition, atmospheric trace gas, and meteorological variables. Comprehensive multi‐campaign airborne measurements, covering varied physicochemical regimes in the troposphere, confirm the validity of and help probe the inner workings of this ML model: revealing for the first time that different ranges of atmospheric aerosol composition and mass correspond to distinct aerosol number size distributions. ML extracts this information, important for accurate quantification of CCN, additionally from both chemistry and meteorology. This can provide a physicochemically explainable, computationally efficient, robust ML pathway in global climate models that only resolve aerosol composition; potentially mitigating the uncertainty of effective radiative forcing due to aerosol‐cloud interactions (ERF_aci) and improving confidence in assessment of anthropogenic contributions and climate change projections.