scholarly journals Exploring DMS oxidation and implications for global aerosol radiative forcing

2021 ◽  
Author(s):  
Ka Ming Fung ◽  
Colette L. Heald ◽  
Jesse H. Kroll ◽  
Siyuan Wang ◽  
Duseong S. Jo ◽  
...  
Keyword(s):  
Gas Phase ◽  
Radiative Forcing ◽  
Dimethyl Sulfide ◽  
Methanesulfonic Acid ◽  
Atmospheric Model ◽  
Cloud Formation ◽  
Aerosol Formation ◽  
Gas Phase Chemistry ◽  
Dms Oxidation ◽  
Phase Chemistry

Abstract. Aerosol indirect radiative forcing (IRF), which characterizes how aerosols alter cloud formation and properties, is very sensitive to the preindustrial (PI) aerosol burden. Dimethyl sulfide (DMS), emitted from the ocean, is a dominant natural precursor of non-sea-salt sulfate in the PI and pristine present-day (PD) atmospheres. Here we revisit the atmospheric oxidation chemistry of DMS, particularly under pristine conditions, and its impact on aerosol IRF. Based on previous laboratory studies, we expand the simplified DMS oxidation scheme used in the Community Atmospheric Model version 6 with chemistry (CAM6-chem) to capture the OH-addition pathway as well as the H-abstraction pathway and the associated isomerization branch. These additional oxidation channels of DMS produce several stable intermediate compounds, e.g., methanesulfonic acid (MSA) and hydroperoxymethyl thioformate (HPMTF), delay the formation of sulfate, and hence, alter the spatial distribution of sulfate aerosol and radiative impacts. The expanded scheme improves the agreement between modeled and observed concentrations of DMS, MSA, HPMTF, and sulfate over most marine regions based on the NASA Atmospheric Tomography (ATom), the Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA), and the VAMOS Ocean-Cloud-Atmosphere-Land Study Regional Experiment (VOCALS-REx) measurements. We find that the global HPMTF burden, as well as the burden of sulfate produced from DMS oxidation are relatively insensitive to the assumed isomerization rate, but the burden of HPMTF is very sensitive to a potential additional cloud loss. We find that global sulfate burden under PI and PD emissions increase to 412 Gg-S (+29 %) and 582 Gg-S (+8.8 %), respectively, compared to the standard simplified DMS oxidation scheme. The resulting annual mean global PD direct radiative effect of DMS-derived sulfate alone is −0.11  W m−2. The enhanced PI sulfate produced via the gas-phase chemistry updates alone dampens the aerosol IRF as anticipated (−2.2 W m−2 in standard versus −1.7 W m−2 with updated gas-phase chemistry). However, high clouds in the tropics and low clouds in the Southern Ocean appear particularly sensitive to the additional aqueous-phase pathways, counteracting this change (−2.3 W m−2). This study confirms the sensitivity of aerosol IRF to the PI aerosol loading, as well as the need to better understand the processes controlling aerosol formation in the PI atmosphere and the cloud response to these changes.

scholarly journals Sparkling nights and very hot days on WASP-18b: the formation of clouds and the emergence of an ionosphere

2019 ◽  
Vol 626 ◽  
pp. A133 ◽  
Author(s):  
Ch. Helling ◽  
P. Gourbin ◽  
P. Woitke ◽  
V. Parmentier
Keyword(s):  
Gas Phase ◽  
Cloud Formation ◽  
Hot Jupiters ◽  
Gas Phase Chemistry ◽  
Formation And Evolution ◽  
Heterogeneous Clouds ◽  
Phase Chemistry ◽  
Equilibrium Calculations ◽  
Very High ◽  
Substantial Degree

Context. WASP-18b is an ultra-hot Jupiter with a temperature difference of up to 2500 K between day and night. Such giant planets begin to emerge as a planetary laboratory for understanding cloud formation and gas chemistry in well-tested parameter regimes in order to better understand planetary mass loss and for linking observed element ratios to planet formation and evolution. Aims. We aim to understand where clouds form, their interaction with the gas-phase chemistry through depletion and enrichment, the ionisation of the atmospheric gas, and the possible emergence of an ionosphere on ultra-hot Jupiters. Methods. We used 1D profiles from a 3D atmosphere simulation for WASP-18b as input for kinetic cloud formation and gas-phase chemical equilibrium calculations. We solved our kinetic cloud formation model for these 1D profiles, which sample the atmosphere of WASP-18b at 16 different locations along the equator and in the mid-latitudes. We derived the gas-phase composition consistently. Results. The dayside of WASP-18b emerges as completely cloud-free as a result of the very high atmospheric temperatures. In contrast, the nightside is covered in geometrically extended and chemically heterogeneous clouds with dispersed particle size distributions. The atmospheric C/O ratio increases to >0.7 and the enrichment of the atmospheric gas with cloud particles is ρd/ρgas > 10−3. The clouds that form at the limbs appear located farther inside the atmosphere, and they are the least extended. Not all day- to nightside terminator regions form clouds. The gas phase is dominated by H2, CO, SiO, H2O, H2S, CH4, and SiS. In addition, the dayside has a substantial degree of ionisation that is due to ions such as Na+, K+, Ca+, and Fe+. Al+ and Ti+ are the most abundant of their element classes. We find that WASP-18b, as one example for ultra-hot Jupiters, develops an ionosphere on the dayside.

scholarly journals An advanced modeling study on the impacts and atmospheric implications of multiphase dimethyl sulfide chemistry

2016 ◽  
Vol 113 (42) ◽  
pp. 11776-11781 ◽  
Author(s):  
Erik Hans Hoffmann ◽  
Andreas Tilgner ◽  
Roland Schrödner ◽  
Peter Bräuer ◽  
Ralf Wolke ◽  
...  
Keyword(s):  
Aqueous Phase ◽  
Dimethyl Sulfide ◽  
Oxidation Products ◽  
Radical Mechanism ◽  
Sulfur Source ◽  
Aerosol Formation ◽  
Detailed Model ◽  
Model Studies ◽  
Dms Oxidation ◽  
Phase Chemistry

Oceans dominate emissions of dimethyl sulfide (DMS), the major natural sulfur source. DMS is important for the formation of non-sea salt sulfate (nss-SO42−) aerosols and secondary particulate matter over oceans and thus, significantly influence global climate. The mechanism of DMS oxidation has accordingly been investigated in several different model studies in the past. However, these studies had restricted oxidation mechanisms that mostly underrepresented important aqueous-phase chemical processes. These neglected but highly effective processes strongly impact direct product yields of DMS oxidation, thereby affecting the climatic influence of aerosols. To address these shortfalls, an extensive multiphase DMS chemistry mechanism, the Chemical Aqueous Phase Radical Mechanism DMS Module 1.0, was developed and used in detailed model investigations of multiphase DMS chemistry in the marine boundary layer. The performed model studies confirmed the importance of aqueous-phase chemistry for the fate of DMS and its oxidation products. Aqueous-phase processes significantly reduce the yield of sulfur dioxide and increase that of methyl sulfonic acid (MSA), which is needed to close the gap between modeled and measured MSA concentrations. Finally, the simulations imply that multiphase DMS oxidation produces equal amounts of MSA and sulfate, a result that has significant implications for nss-SO42− aerosol formation, cloud condensation nuclei concentration, and cloud albedo over oceans. Our findings show the deficiencies of parameterizations currently used in higher-scale models, which only treat gas-phase chemistry. Overall, this study shows that treatment of DMS chemistry in both gas and aqueous phases is essential to improve the accuracy of model predictions.

scholarly journals Kinetic modelling of formation and evaporation of SOA from NO<sub>3</sub> oxidation of pure and mixed monoterpenes

2020 ◽  
Author(s):  
Thomas Berkemeier ◽  
Masayuki Takeuchi ◽  
Gamze Eris ◽  
Nga L. Ng
Keyword(s):  
Kinetic Model ◽  
Gas Phase ◽  
Organic Aerosol ◽  
Chemical Mechanism ◽  
Phase Reaction ◽  
Particle Phase ◽  
Aerosol Formation ◽  
Environmental Chamber ◽  
Gas Phase Chemistry ◽  
Phase Chemistry

Abstract. Organic aerosol constitutes a major fraction of the global aerosol burden and is predominantly formed as secondary organic aerosol (SOA). Environmental chambers have been used extensively to study aerosol formation and evolution under controlled conditions similar to the atmosphere, but quantitative prediction of the outcome of these experiments is generally not achieved, which signifies our lack in understanding of these results and limits their portability to large scale models. In general, kinetic models employing state-of-the-art explicit chemical mechanisms fail to describe the mass concentration and composition of SOA obtained from chamber experiments. Specifically, chemical reactions involving nitrate radical (NO3) oxidation of volatile organic compounds (VOCs) are a source of major uncertainty for assessing the chemical and physical properties of oxidation products. Here, we introduce a kinetic model that treats gas-phase chemistry, gas-particle partitioning, particle-phase oligomerization, and chamber wall loss and use it to describe the oxidation of the monoterpenes α-pinene and limonene with NO3. The model can reproduce aerosol mass and nitration degrees in experiments using either pure precursors or their mixtures and infers volatility distributions of products, branching ratios of reactive intermediates as well as particle-phase reaction rates. The gas-phase chemistry in the model is based on the Master Chemical Mechanism (MCM), but trades speciation of single compounds for the overall ability of quantitatively describing SOA formation by using a lumped chemical mechanism. The complex branching into a multitude of individual products in MCM is replaced in this model with product volatility distributions, detailed peroxy (RO2) and alkoxy (RO) radical chemistry and amended by a particle-phase oligomerization scheme. The kinetic parameters obtained in this study are constrained by a set of SOA formation and evaporation experiments conducted in the Georgia Tech Environmental Chamber (GTEC) facility. For both precursors, we present volatility distributions of nitrated and non-nitrated reaction products that are obtained by fitting the kinetic model systematically to the experimental data using a global optimization method, the Monte Carlo Genetic Algorithm (MCGA). The results presented here provide new mechanistic insight into the processes leading to formation and evaporation of SOA. Most notably, much of the non-linear behavior of precursor mixtures can be understood by RO2 fate and reversible oligomerization reactions in the particle phase, but some effects could be accredited to kinetic limitations of mass transport in the particle phase. The methodologies described in this work provide a basis for quantitative analysis of multi-source data from environmental chamber experiments with manageable computational effort.

scholarly journals Application of random forest regression to the calculation of gas-phase chemistry within the GEOS-Chem chemistry model v10

2019 ◽  
Vol 12 (3) ◽  
pp. 1209-1225 ◽  
Author(s):  
Christoph A. Keller ◽  
Mat J. Evans
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Gas Phase ◽  
Atmospheric Chemistry ◽  
Random Forest Regression ◽  
Data Set ◽  
Gas Phase Chemistry ◽  
Chemical Conditions ◽  
Phase Chemistry ◽  
The Impact

Abstract. Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models are numerically intense, and previous attempts to reduce the numerical cost of chemistry solvers have not delivered transformative change. We show here the potential of a machine learning (in this case random forest regression) replacement for the gas-phase chemistry in atmospheric chemistry transport models. Our training data consist of 1 month (July 2013) of output of chemical conditions together with the model physical state, produced from the GEOS-Chem chemistry model v10. From this data set we train random forest regression models to predict the concentration of each transported species after the integrator, based on the physical and chemical conditions before the integrator. The choice of prediction type has a strong impact on the skill of the regression model. We find best results from predicting the change in concentration for long-lived species and the absolute concentration for short-lived species. We also find improvements from a simple implementation of chemical families (NOx = NO + NO2). We then implement the trained random forest predictors back into GEOS-Chem to replace the numerical integrator. The machine-learning-driven GEOS-Chem model compares well to the standard simulation. For ozone (O3), errors from using the random forests (compared to the reference simulation) grow slowly and after 5 days the normalized mean bias (NMB), root mean square error (RMSE) and R2 are 4.2 %, 35 % and 0.9, respectively; after 30 days the errors increase to 13 %, 67 % and 0.75, respectively. The biases become largest in remote areas such as the tropical Pacific where errors in the chemistry can accumulate with little balancing influence from emissions or deposition. Over polluted regions the model error is less than 10 % and has significant fidelity in following the time series of the full model. Modelled NOx shows similar features, with the most significant errors occurring in remote locations far from recent emissions. For other species such as inorganic bromine species and short-lived nitrogen species, errors become large, with NMB, RMSE and R2 reaching >2100 % >400 % and <0.1, respectively. This proof-of-concept implementation takes 1.8 times more time than the direct integration of the differential equations, but optimization and software engineering should allow substantial increases in speed. We discuss potential improvements in the implementation, some of its advantages from both a software and hardware perspective, its limitations, and its applicability to operational air quality activities.

Gas Phase Chemistry in Cellulose Fast Pyrolysis

2009 ◽  
Vol 48 (3) ◽  
pp. 1391-1399 ◽  
Author(s):  
R. Lanza ◽  
D. Dalle Nogare ◽  
P. Canu
Keyword(s):  
Gas Phase ◽  
Fast Pyrolysis ◽  
Gas Phase Chemistry ◽  
Phase Chemistry
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