Aerosol Microphysics Research Articles

Sensitivities of Large Eddy Simulations of Aerosol Plume Transport and Cloud Response

AbstractCloud responses to surface‐based sources of aerosol perturbation partially depend on how turbulent transport of the aerosol to cloud base affects the spatial and temporal distribution of aerosol. Here, scenarios of plume injection below a marine stratocumulus cloud are modeled using large eddy simulations coupled to a prognostic bulk aerosol and cloud microphysics scheme. Both passive plumes, consisting of an inert tracer, and active plumes are investigated, where the latter are representative of saltwater droplet plumes such as have been proposed for marine cloud brightening. Passive plume scenarios show higher in‐plume cloud brightness (relative to out‐of‐plume) due to the predominant transport of the passive plume tracer from the near‐surface to the cloud layer within updrafts. These updrafts rise into brighter areas within the cloud deck, even in the absence of an aerosol perturbation associated with an active plume. Comparing albedo at in‐plume to out‐of‐plume locations associates the inert plume with the brightest cloud locations, without any causal effect of the plume on the cloud. Numerical sensitivities are first assessed to establish a suitable model configuration. Then sensitivity to particle injection rate is investigated. Trade‐offs are identified between the number of injected particles and the suppressive effect of droplet evaporation on plume loft and spread. Furthermore, as the near‐field in‐plume brightening effect does not depend significantly on injection rate given a suitable definition of perturbed versus unperturbed regions of the flow, plume area is a key controlling factor on the overall cloud brightening effect of an aerosol perturbation.

In Situ Characterization of Dust Storms and Their Snow‐Darkening Effect Over Himalayas

AbstractIn this study, we used satellite observations to identify 10 typical dust‐loading events over the Indian Himalayas. Next, the aerosol microphysical and optical properties during these identified dust storms are characterized using cotemporal in situ measurements over Mukteshwar, a representative site in Indian Himalayas. Relative to the background values, the mass of coarse particles (size range between 2.5 and 10 μm) and the extinction coefficient were found to be enhanced by 400% (from 24 ± 15 to 98 ± 40 μg/m3) and 175% (from 89 ± 57 Mm−1 to 156 ± 79 Mm−1), respectively, during these premonsoonal dust‐loading events. Moreover, based on the air mass trajectory, these dust storms can be categorized into two categories: (a) mineral dust events (MDEs), which involve long‐range transported dust plumes traversing through the lower troposphere to reach the Himalayas and (b) polluted dust events (PDEs), which involve short‐range transported dust plumes originating from the arid western regions of the Indian subcontinent and traveling within the heavily polluted boundary layer of the Gangetic plains before reaching the Himalayas. Interestingly, compared to the background, the SSA and AAE decrease during PDEs but increase during MDEs. More importantly, we observe a twofold increase in black carbon concentrations and the aerosol absorption coefficient (relative to the background values) during the PDEs with negligible changes during MDEs. Consequently, the aerosol‐induced snow albedo reduction (SAR) also doubles during MDEs and PDEs relative to background conditions. Thus, our findings provide robust observational evidence of substantial dust‐induced snow and glacier melting over the Himalayas.

High Concentrations of Nanoparticles From Isoprene Nitrates Predicted in Convective Outflow Over the Amazon

AbstractThe biogenic volatile organic compounds isoprene and α‐pinene are abundant over the Amazon and can be efficiently transported to the upper troposphere by deep convective clouds (DCC). We simulate their transport and chemistry following DCC updrafts and upper tropospheric outflow using a multi‐phase chemistry model with aerosol microphysics constrained by recent field measurements. In the lightning‐ and NO‐rich early morning outflow, organonitrates dominate the predicted ultra‐ and extremely‐low‐volatility organic compounds (ULVOCs+) derived from isoprene and α‐pinene. Nucleation of particles by α‐pinene‐derived ULVOCs+ alone, with an associated formation rate of 1.7 nm molecular clusters of 0.0006 s−1 cm−3 and resulting maximum particle number concentration of 19 cm−3, is not sufficient to explain ultrafine aerosol abundances observed in Amazonian DCC outflow. When isoprene‐derived ULVOCs+ are allowed to contribute to nucleation, the new particle formation rate increases by six orders of magnitude, and the predicted number concentrations reach 104 cm−3, consistent with observations.

Investigations on Stubble-Burning Aerosols over a Rural Location Using Ground-Based, Model, and Spaceborne Data

Agriculture crop residue burning has become a major environmental problem facing the Indo-Gangetic plain, as well as contributing to global warming. This paper reports the results of a comprehensive study, examining the variations in aerosol optical, microphysical, and radiative properties that occur during biomass-burning events at Amity University Haryana (AUH), at a rural station in Gurugram (Latitude: 28.31° N, Longitude: 76.90° E, 285 m AMSL), employing ground-based observations of AERONET and Aethalometer, as well as satellite and model simulations during 7–16 November 2021. The smoke emissions during the burning events enhanced the aerosol optical depth (AOD) and increased the Angstrom exponent (AE), suggesting the dominance of fine-mode aerosols. A smoke event that affected the study region on 11 November 2021 is simulated using the regional NAAPS model to assess the role of smoke in regional aerosol loading that caused an atmospheric forcing of 230.4 W/m2. The higher values of BC (black carbon) and BB (biomass burning), and lower values of AAE (absorption Angstrom exponent) are also observed during the peak intensity of the smoke-event period. A notable layer of smoke has been observed, extending from the surface up to an altitude of approximately 3 km. In addition, the observations gathered from CALIPSO regarding the vertical profiles of aerosols show a qualitative agreement with the values obtained from AERONET observations. Further, the smoke plumes that arose due to transport of a wide-spread agricultural crop residue burning are observed nationwide, as shown by MODIS imagery, and HYSPLIT back trajectories. Thus, the present study highlights that the smoke aerosol emissions during crop residue burning occasions play a critical role in the local/regional aerosol microphysical and radiation properties, and hence in the climate variability.

Impact of aerosols on atmospheric electrification over East and West Africa

Studies have shown that atmospheric aerosols can modify cloud microphysics. The influence of atmospheric aerosols on mechanisms that lead to generation of lightning is very complex and not fully understood. Recent studies have also revealed that, west Africa has high concentration of atmospheric aerosols due to localized wind which diverge from Sahara desert to this area. This study investigates the impact of atmospheric aerosols on lightning flash rate over east and west Africa by utilizing aerosol optical depth (AOD) from Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis data, convective available potential energy (CAPE), potential temperature, surface relative humidity, clouds and lightning flash rate. Pearson correlation and partial correlation have been applied between lightning flash rate and AOD, humidity, clouds, CAPE as well as potential temperature. Quantitative results show that there is a strong positive correlation (∼0.75) between lightning flash rate and aerosols under low concentration of aerosols (AOD ≤ 0.6) due to aerosol microphysics effect. In the presence of high aerosol concentration (AOD > 0.6), the correlation coefficient between lightning flash rate and aerosols is somehow weak (∼0.45) due to decrease in the number of ice particles as well as radiation effect of aerosols. However, the correlation coefficient between lightning flash rate and CAPE, clouds and potential temperature are all positive under both high and low concentration of atmospheric aerosols.

Global Influence of Organic Aerosol Volatility on Aerosol Microphysical Processes: Composition and Number

AbstractWe present MATRIX‐VBS, a new aerosol scheme that simulates organic partitioning in an aerosol microphysics model, as part of the NASA GISS ModelE Earth System Model. MATRIX‐VBS builds on its predecessor aerosol microphysics model MATRIX (Bauer et al., 2008, https://doi.org/10.5194/acp‐8‐6003‐2008) and was developed in the box model framework (Gao et al., 2017, https://doi.org/10.5194/gmd‐10‐751‐2017). The scheme features the inclusion of organic partitioning between the gas and particle phases and the photochemical aging process using the volatility‐basis set (Donahue et al., 2006, https://doi.org/10.1021/es052297c). To assess the performance of the new model, we compared its mass concentration, number concentration, and activated number concentration to the original scheme MATRIX, and evaluated its mass concentrations in four seasons against data from the NASA Atmospheric Tomography Mission (ATom) aircraft campaign. Results from MATRIX‐VBS show that organics are transported further away from their source, and their mass concentration increases aloft and decreases at the surface compared to those in MATRIX. The mass concentration of organics at the surface agrees well with measurements, and there are discrepancies for vertical profiles aloft. In the new scheme, the global mass load of organic aerosols increased by 50%, there is also an increased number of particles at the surface and fewer activated ones in most regions. The new scheme presents advanced and more comprehensive capability in simulating aerosol processes.

Impact of cloud properties and aerosols on the spatial distribution of Indian summer monsoon rainfall: A case study

Quantitative seasonal Indian summer monsoon (ISM) rainfall forecasting has been a challenge for the climate models as it is sensitive to cloud properties, which are modulated by large-scale dynamics. Proper understanding of the role of dynamics, aerosols, and cloud microphysical processes is important for the precipitation calculation in dynamical models. The all-India averaged seasonal ISM rainfall (ISMR) from June to September had been normal or above normal, however there were large spatial variations. Some meteorological subdivisions, the majority of which lie in the Gangetic Plains, received deficient seasonal rainfall during recent years 2022 and 2010. The quantitative seasonal prediction of the spatial distribution of ISMR from global coupled models (GCMs) has also shown a hard time for accurate spatial rainfall distribution. Four monsoon years (2022, 2021, 2011, and 2010) are considered, in which the country as a whole (all India land points) was ‘above normal’ or ‘normal’. However, in two specific years (2022 and 2010), some subdivisions received deficient rainfall (17% and 15% of the total area). We have used several datasets from the ground, and satellite observations, along with reanalysis products and computed climatology and anomalies for monsoon years of 2022 and 2010. The low-level wind and vertical velocity along with cloud microphysics played a pivotal role in the heterogeneous distribution of ISMR during 2022 and 2010 as compared to years 2021 and 2011. Here, we have demonstrated that the spatial heterogeneity of the positive and negative precipitation anomalies was consistent with cloud properties. Aerosols might have an impact on the cloud microphysical processes and modulate cloud properties and rainfall over different regions due to the availability of water vapor. Therefore, the inclusion of aerosol indirect effects in climate models is crucial for better interactions among dynamics, aerosol, and cloud microphysics. Proper representation of cloud droplet size distribution and microphysical conversion rates might be helpful for quantitative seasonal ISMR forecasting at a smaller spatial scale.

Estimating the Impact of a 2017 Smoke Plume on Surface Climate Over Northern Canada With a Climate Model, Satellite Retrievals, and Weather Forecasts

AbstractIn August 2017, a smoke plume from wildfires in British Columbia and the Northwest Territories recirculated and persisted over northern Canada for over two weeks. We compared a full‐factorial set of NASA Goddard Institute for Space Studies ModelE simulations of the plume to satellite retrievals of aerosol optical depth and carbon monoxide, finding that ModelE performance was dependent on the model configuration, and more so on the choice of injection height approach, aerosol scheme and biomass burning emissions estimates than to the choice of horizontal winds for nudging. In particular, ModelE simulations with free‐tropospheric smoke injection, a mass‐based aerosol scheme and comparatively high fire NOx emissions led to unrealistically high aerosol optical depth. Using paired simulations with and without fire emissions, we estimated that for 16 days over an 850,000 km2 region, the smoke decreased planetary boundary layer heights by between 253 and 547 m, decreased downward shortwave radiation by between 52 and 172 Wm−2, and decreased surface temperature by between 1.5°C and 4.9°C, the latter spanning an independent estimate from operational weather forecasts of a 3.7°C cooling. The strongest surface climate effects were for ModelE configurations with more detailed aerosol microphysics that led to a stronger first indirect effect.

A Potential Surface Warming Regime for Volcanic Super‐Eruptions Through Stratospheric Water Vapor Increases

AbstractLarge volcanic eruptions are known to influence the climate through a variety of mechanisms including aerosol‐forced cooling and warming via emitted CO2. The January 2022 Hunga shallow underwater eruption caused an increase in stratospheric water vapor, and demonstrated how the associated positive radiative forcing can be an important component of an eruption's climate forcing. We present interactive stratospheric aerosol model simulations of super‐volcanic eruptions with a range of SO2 emissions that can produce climate warming through feedback effects produced by a large igneous province (or “flood basalt”) mid‐latitude super‐eruption using Goddard Earth Observing System Chemistry Climate Model climate model simulations. The model experiments suggest total SO2 emissions ≳4,000 Tg/4 Gt generate a multi‐year period of sustained aerosol absorptive local‐heating of the upper troposphere and lower stratosphere and hence produce net climate warming after strong initial cooling. The eruptions produce stratospheric water vapor increases of factors of 8–600. The initiation of these feedbacks within the simulations suggest they could occur for individual stratovolcano eruptions of the scale of the Toba or Tambora eruptions. We note the sensitivity of our results to volcanic sulfate aerosol microphysics and model chemistry.

Modeling the evolution of aerosol particles from a radiological dispersal device

Radiological dispersal devices (RDDs) have a potential to disperse radioactive aerosols into the atmosphere through explosions. Accurately estimating the respirable fraction of these aerosols through experiments presents a considerable challenge. Also, the modeling of aerosol particle evolution stemming from an RDD explosion is inherently complex, involving the interplay of various physical processes operating at different time scales. In this study, we propose a comprehensive numerical model to estimate the respirable fraction of aerosols generated during RDD explosions, integrating the thermodynamic properties of detonation products with microphysical aerosol processes. The model assumes particles are spherical and ignores charge effects. It is also assumed that the thermodynamic properties of the cloud are uniform within its volume and that thermal equilibrium exists between particles and the surrounding medium. Numerical simulations are conducted for diverse experimental scenarios, and performance of the model is assessed by comparing its predictions with experimental data pertaining to Carbon and Cobalt particles. Notably, the model predicts the average particle diameter of Carbon particles within the detonation front of TNT at 13.8 nm, closely matching the experimental observation of 13 nm (Rubtsov et al., 2019). Additionally, the model captures the peak value of the cobalt particle mass fraction distribution, approximating it to be around 0.7μm, in agreement with experimental findings (Di Lemma et al., 2016). These findings indicate that the proposed model is capable of predicting the behavior of both radioactive and non-radioactive aerosols. Also, this study underscores the potential of modeling approaches in addressing existing knowledge gaps related to RDDs, thereby contributing to enhanced impact assessment and management strategies for incidents involving RDDs.

A Modified Look-Up Table Based Algorithm with a Self-Posed Scheme for Fine-Mode Aerosol Microphysical Properties Inversion by Multi-Wavelength Lidar

Aerosol microphysical properties, including aerosol particle size distribution, complex refractive index and concentration properties, are key parameters evaluating the impact of aerosols on climate, meteorology, and human health. High Spectral Resolution Lidar (HSRL) is an efficient tool for probing the vertical optical properties of aerosol particles, including the aerosol backscatter coefficient (β) and extinction coefficient (α), at multiple wavelengths. To swiftly process vast data volumes, address the ill-posedness of retrieval problems, and suit simpler lidar systems, this study proposes an algorithm (modified algorithm) for retrieving microphysical property profiles from the HSRL optical data targeting fine-mode aerosols, building upon a previous algorithm (basic algorithm). The modified algorithm is based on a look-up table (LUT) approach, combined with the k-nearest neighbor (k-NN) and random forest (RF) algorithms, and it optimizes the decision tree generation strategy, incorporating a self-posed scheme. In numerical simulation tests for different lidar configurations, the modified algorithm reduced retrieval errors by 41%, 30%, and 32% compared to the basic algorithm for 3β + 2α, 3β + 1α, and 2β + 1α, respectively, with a remarkable improvement of stability. In two observation scenes of a field campaign, the median relative errors of the effective radius for 3β + 2α were 6% and −3%, and the median absolute errors of single-scattering albedo were 0.012 and 0.005. This method represents a further step toward the use of the LUT approach, with the potential to provide effective and efficient aerosol microphysical retrieval for simpler lidar systems, which could advance our understanding of aerosols’ climatic, meteorological, and health impacts.

Measurement report: Cloud and environmental properties associated with aggregated shallow marine cumulus and cumulus congestus

Abstract. Mesoscale organization of marine convective clouds into linear or clustered states is prevalent across the tropical and subtropical oceans, and its investigation served as a guiding focus for a series of process study flights conducted as part of the Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) during summer 2020, 2021, and 2022. These select ACTIVATE flights involved a novel strategy for coordinating two aircraft, with respective remote sensing and in situ sampling payloads, to probe regions of organized shallow convection for several hours. The main purpose of this measurement report is to summarize the aircraft sampling approach, describe the characteristics and evolution of the cases, and provide an overview of the datasets that can serve as a starting point for more detailed modeling and analysis studies. Six flights are described, involving a total of 80 dropsonde profiles that capture the environment surrounding clustered shallow convection. The flights include detailed observations of the vertical structure of cloud systems, comprising up to 20 in situ sampling levels. Four cases involved deepening convection rooted in the marine boundary layer that developed vertically to 2–5 km with varying precipitation amounts, while two cases captured more complex and developed cumulus congestus systems extending above 5 km. In addition to the thermodynamic and dynamic characterization afforded by dropsonde and in situ measurements, the datasets include cloud and aerosol microphysics, trace gas concentrations, aerosol and droplet composition, and cloud and aerosol remote sensing from high-spectral-resolution lidar and polarimetry.

MieAI: a neural network for calculating optical properties of internally mixed aerosol in atmospheric models

Aerosols influence weather and climate by interacting with radiation through absorption and scattering. These effects heavily rely on the optical properties of aerosols, which are mainly governed by attributes such as morphology, size distribution, and chemical composition. These attributes undergo continuous changes due to chemical reactions and aerosol micro-physics, resulting in significant spatio-temporal variations. Most atmospheric models struggle to incorporate this variability because they use pre-calculated tables to handle aerosol optics. This offline approach often leads to substantial errors in estimating the radiative impacts of aerosols along with posing significant computational burdens. To address this challenge, we introduce a computationally efficient and robust machine learning approach called MieAI. It allows for relatively inexpensive calculation of the optical properties of internally mixed aerosols with a log-normal size distribution. Importantly, MieAI fully incorporates the variability in aerosol chemistry and microphysics. Our evaluation of MieAI against traditional Mie calculations, using number concentrations from the ICOsahedral Nonhydrostatic model with Aerosol and Reactive Trace gases (ICON-ART) simulations, demonstrates that MieAI exhibits excellent predictive accuracy for aerosol optical properties. MieAI achieves this with errors well within 10%, and it operates more than 1000 times faster than the benchmark approach of Mie calculations. Due to its generalized nature, the MieAI approach can be implemented in any chemistry transport model which represents aerosol size distribution in the form of log-normally distributed internally mixed modes. This advancement has the potential to replace frequently employed look-up tables and plays a substantial role in the ongoing attempts to reduce uncertainties in estimating aerosol radiative forcing.

Dependency of the impacts of geoengineering on the stratospheric sulfur injection strategy – Part 2: How changes in the hydrological cycle depend on the injection rate and model used

Abstract. This is the second of two papers in which we study the dependency of the impacts of stratospheric sulfur injections on the model and injection strategy used. Here, aerosol optical properties from simulated stratospheric aerosol injections using two aerosol models (modal scheme M7 and sectional scheme SALSA), as described in Part 1 (Laakso et al., 2022), are implemented consistently into the EC-Earth, MPI-ESM and CESM Earth system models (ESMs) to simulate the climate impacts of different injection rates ranging from 2 to 100 Tg(S) yr−1. Two sets of simulations were run with the three ESMs: (1) regression simulations, in which an abrupt change in CO2 concentration or stratospheric aerosols over pre-industrial conditions was applied to quantify global mean fast temperature-independent climate responses and quasi-linear dependence on temperature, and (2) equilibrium simulations, in which radiative forcing of aerosol injections with various magnitudes compensated for the corresponding radiative forcing of CO2 enhancement to study the dependence of precipitation on the injection magnitude. The latter also allow one to explore the regional climatic responses. Large differences in SALSA- and M7-simulated radiative forcing in Part 1 translated into large differences in the estimated surface temperature and precipitation changes in ESM simulations; for example, an injection rate of 20 Tg(S) yr−1 in CESM using M7-simulated aerosols led to only 2.2 K global mean cooling, while EC-Earth–SALSA combination produced a 5.2 K change. In equilibrium simulations, where aerosol injections were utilized to offset the radiative forcing caused by an atmospheric CO2 concentration of 500 ppm, the decrease in global mean precipitation varied among models, ranging from −0.7 % to −2.4 % compared with the pre-industrial climate. These precipitation changes can be explained by the fast precipitation response due to radiation changes caused by the stratospheric aerosols and CO2, as the global mean fast precipitation response is shown to be negatively correlated with global mean atmospheric absorption. Our study shows that estimating the impact of stratospheric aerosol injection on climate is not straightforward. This is because the simulated capability of the sulfate layer to reflect solar radiation and absorb long-wave radiation is sensitive to the injection rate as well as the aerosol model used to simulate the aerosol field. These findings emphasize the necessity for precise simulation of aerosol microphysics to accurately estimate the climate impacts of stratospheric sulfur intervention. This study also reveals gaps in our understanding and uncertainties that still exist related to these controversial techniques.

Inactivation Mechanisms of Escherichia coli in Simulants of Respiratory and Environmental Aerosol Droplets

The airborne transmission of disease relies on the ability of microbes to survive aerosol transport and, subsequently, cause infection when interacting with a host. The length of time airborne microorganisms remain infectious in aerosol droplets is a function of numerous variables. We present measurements of mass and heat transfer from liquid aerosol droplets combined with airborne survival data for Escherichia coli MRE162, an ACDP category 1 microorganism used as a model system, under a wide range of environmental conditions, droplet compositions and microbiological conditions. In tandem, these companion measurements demonstrate the importance of understanding the complex relationship between aerosol microphysics and microbe survival. Specifically, our data consist of the correlation of a wide range of physicochemical properties (e.g., evaporation rates, equilibrium water content, droplet morphology, compositional changes in droplet solute and gas phase, etc.), with airborne viability decay to infer the impact of aerosol microphysics on airborne bacterial survival. Thus, a mechanistic approach to support prediction of the survival of microorganisms in the aerosol phase as a function of biological, microphysical, environmental, and experimental (aerosol-generation and sampling) processes is presented. Specific findings include the following: surfactants do not increase bacteria stability in aerosol, while both the bacteria growth phase and bacteria concentration may affect the rate at which bacteria decay in aerosol.

Contribution of expanded marine sulfur chemistry to the seasonal variability of dimethyl sulfide oxidation products and size-resolved sulfate aerosol

Abstract. Marine emissions of dimethyl sulfide (DMS) and the subsequent formation of its oxidation products methanesulfonic acid (MSA) and sulfuric acid (H2SO4) are well-known natural precursors of atmospheric aerosols, contributing to particle mass and cloud formation over ocean and coastal regions. Despite a long-recognized and well-studied role in the marine troposphere, DMS oxidation chemistry remains a work in progress within many current air quality and climate models, with recent advances exploring heterogeneous chemistry and uncovering previously unknown intermediate species. With the identification of additional DMS oxidation pathways and intermediate species that influence the eventual fate of DMS, it is important to understand the impact of these pathways on the overall sulfate aerosol budget and aerosol size distribution. In this work, we update and evaluate the DMS oxidation mechanism of the chemical transport model GEOS-Chem by implementing expanded DMS oxidation pathways in the model. These updates include gas- and aqueous-phase reactions, the formation of the intermediates dimethyl sulfoxide (DMSO) and methanesulfinic acid (MSIA), and cloud loss and aerosol uptake of the recently quantified intermediate hydroperoxymethyl thioformate (HPMTF). We find that this updated mechanism collectively decreases the global mean surface-layer gas-phase sulfur dioxide (SO2) mixing ratio by 40 % and enhances the sulfate aerosol (SO42-) mixing ratio by 17 %. We further perform sensitivity analyses exploring the contribution of cloud loss and aerosol uptake of HPMTF to the overall sulfur budget. Comparing modeled concentrations to available observations, we find improved biases relative to previous studies. To quantify the impacts of these chemistry updates on global particle size distributions and the mass concentration, we use the TwO-Moment Aerosol Sectional (TOMAS) aerosol microphysics module coupled to GEOS-Chem and find that changes in particle formation and growth affect the size distribution of aerosol. With this new DMS-oxidation scheme, the global annual mean surface-layer number concentration of particles with diameters smaller than 80 nm decreases by 16.8 %, with cloud loss processes related to HPMTF being mostly responsible for this reduction. However, the global annual mean number of particles larger than 80 nm (corresponding to particles capable of acting as cloud condensation nuclei, CCN) increases by 3.8 %, suggesting that the new scheme promotes seasonal particle growth to these sizes.

Simulating the Volcanic Sulfate Aerosols From the 1991 Eruption of Cerro Hudson and Their Impact on the 1991 Ozone Hole

AbstractThe Chilean volcano Cerro Hudson erupted between August 8th and 15th, 1991, injecting between 1.7 and 2.9 Tg of SO2 into the upper troposphere and lower stratosphere. We simulate this injection using the Goddard Earth Observing System Earth system model with detailed sulfur chemistry and sectional aerosol microphysics, focusing on the resulting aerosols and their contribution to the 1991 Antarctic Austral Springtime ozone hole. The simulations show a column ozone deficit (12 DU) in the Southern Hemisphere vortex collar region. The majority of this effect is between 10 and 20 km and due to heterogeneous chemistry. The model shows a 26% decrease in ozone from background levels at these altitudes, compared with in‐situ observations of a 50% decrease. Above 20 km, the dynamical response to the eruption also causes lower ozone values, a novel modeling result. This experiment highlights potential interactions between proposed solar radiation management geoengineering aerosols and volcanic eruptions.

Bridging New Observational Capabilities and Process-Level Simulation: Insights into Aerosol Roles in the Earth System

Abstract The spatial distribution of ambient aerosol particles significantly impacts aerosol–radiation–cloud interactions, which contribute to the largest uncertainty in global anthropogenic radiative forcing estimations. However, the atmospheric boundary layer and lower free troposphere have not been adequately sampled in terms of spatiotemporal resolution, hindering a comprehensive characterization of various atmospheric processes and impeding our understanding of the Earth system. To address this research data gap, we have leveraged the development of uncrewed aerial systems (UAS) and advanced measurement techniques to obtain mesoscale spatial data on aerosol microphysical and optical properties around the U.S. Southern Great Plains (SGP) atmospheric observatory. Our study also benefits from state-of-the-art laboratory facilities that include three-dimensional molecular imaging techniques enabled by secondary ion mass spectrometry and nanogram-level chemical composition analysis via micronebulization aerosol mass spectrometry. Through our study, we have developed a framework for observation–modeling integration, enabling an examination of how various assumptions about the organic–inorganic components mixing state, inferred from chemical analysis, affect clouds and radiation in observation-constrained model simulations. By integrating observational constraints (derived from offline chemical analysis of the aerosol surface using collected samples) with in situ UAS observations, we have identified a prominent role of organic-enriched nanometer layers located at the surface of aerosol particles in determining profiles of aerosol optical and hygroscopic properties over the SGP observatory. Furthermore, we have improved the agreement between predicted clouds and ground-based cloud lidar measurements. This UAS–model–laboratory integration exemplifies how these new advanced capabilities can significantly enhance our understanding of aerosol–radiation–cloud interactions.

Influence of More Mechanistic Representation of Particle Dry Deposition on 1850–2000 Changes in Global Aerosol Burdens and Radiative Forcing

AbstractRobust estimates of historical changes in aerosols are key for accurate constraints on climate sensitivity. Dry deposition is a primary sink of aerosols from the atmosphere. However, most global climate models do not accurately represent observed strong dependencies of dry deposition following turbulent transport on aerosol size. It is unclear whether there is a substantial impact of mischaracterized aerosol deposition velocities on historical aerosol changes. Here we describe improved mechanistic representation of aerosol dry deposition in the NASA Goddard Institute for Space Studies (GISS) global climate model, ModelE, and illustrate the impact on 1850–2000 changes in global aerosol burdens as well as aerosol direct and cloud albedo effects using a set of 1850 and 2000 time slice simulations. We employ two aerosol configurations of ModelE (a “bulk” mass‐based configuration and a configuration that more explicitly represents aerosol size distributions, internal mixing, and microphysics) to explore how model structural differences in aerosol representation alter the response to representation of dry deposition. Both configurations show larger historical increases in the global burdens of non‐dust aerosols with the new dry deposition scheme, by 11% in the simpler mass‐based configuration and 23% in the more complex microphysical configuration. Historical radiative forcing responses, which vary in magnitude from 5% to 12% as well as sign, depend on the aerosol configuration.

Improving Estimates of Dynamic Global Marine DMS and Implications for Aerosol Radiative Effect

AbstractDimethyl sulfide (DMS) is the predominant natural sulfur source and plays a pivotal role in regulating global climate. However, the current method for estimating seawater DMS concentrations has limitations, and the existing DMS‐induced radiative effect heavily relies on bottom‐up DMS climatologies. This study aims to improve the method for estimating seawater DMS concentrations as well as to evaluate its induced aerosol direct radiative effect (DRE) and indirect radiative effect (IRE) using a state‐of‐the‐art aerosol microphysics scheme integrated with a chemical transport model. The predicted seawater DMS concentrations based on data‐driven methods were verified with multi‐year in situ measurements, revealing a marked reduction in mean bias by over 80%. Results show that our estimates generally indicate lower seawater DMS concentrations (1.48–1.88 μmol/m3) compared to previous seawater DMS climatologies, with differences ranging from −37% to 11%, and that interannual variability in DMS concentrations is varies significantly, particularly in polar regions. The DRE and cloud‐albedo IRE induced by DMS were −0.06 and −0.19 W/m2, respectively, representing a cooling effect on radiative effect that was weaker by 31.4% and 27.0% of those derived from the commonly used bottom‐up DMS climatology. The comprehensive evaluation of the model's performance of atmospheric DMS prediction based on global‐scale observations shows a significant improvement after using our estimates. Thus, we conclude that the global DMS fluxes provided in the past are overestimated, including its resulting DMS radiative effect, which highlights the need for refining the estimation of global aerosol radiative effect to enhance the accuracy of assessing aerosol‐induced climate impacts.