scholarly journals A Review of the Representation of Aerosol Mixing State in Atmospheric Models

Atmosphere ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 168 ◽  
Author(s):  
Robin Stevens ◽  
Ashu Dastoor
Keyword(s):  
Optical Properties ◽  
Climate Models ◽  
Research Effort ◽  
Cloud Condensation Nuclei ◽  
Weather Prediction ◽  
Heterogeneous Chemistry ◽  
Computationally Efficient ◽  
Atmospheric Models ◽  
Mixing State ◽  
Aerosol Mixing State

Aerosol mixing state significantly affects concentrations of cloud condensation nuclei (CCN), wet removal rates, thermodynamic properties, heterogeneous chemistry, and aerosol optical properties, with implications for human health and climate. Over the last two decades, significant research effort has gone into finding computationally-efficient methods for representing the most important aspects of aerosol mixing state in air pollution, weather prediction, and climate models. In this review, we summarize the interactions between mixing-state and aerosol hygroscopicity, optical properties, equilibrium thermodynamics and heterogeneous chemistry. We focus on the effects of simplified assumptions of aerosol mixing state on CCN concentrations, wet deposition, and aerosol absorption. We also summarize previous approaches for representing aerosol mixing state in atmospheric models, and we make recommendations regarding the representation of aerosol mixing state in future modelling studies.

scholarly journals Toward Reduced Representation of Mixing State for Simulating Aerosol Effects on Climate

2017 ◽  
Vol 98 (5) ◽  
pp. 971-980 ◽  
Author(s):  
Laura Fierce ◽  
Nicole Riemer ◽  
Tami C. Bond
Keyword(s):  
Optical Properties ◽  
Cloud Condensation Nuclei ◽  
Mixing Time ◽  
Radiative Properties ◽  
Mixing State ◽  
Hygroscopic Properties ◽  
Model Aerosol ◽  
Aerosol Effects ◽  
Aerosol Mixing State ◽  
Individual Particles

Abstract Atmospheric aerosols affect Earth’s energy budget, and hence its climate, by scattering and absorbing solar radiation and by altering the radiative properties and the lifetime of clouds. These two major aerosol effects depend on the optical properties and the cloud-nucleating ability of individual particles, which, in turn, depend on the distribution of components among individual particles, termed the “aerosol mixing state.” Global models have moved toward including aerosol schemes to represent the evolution of particle characteristics, but individual particle properties cannot be resolved in global-scale simulations. Instead, models approximate the aerosol mixing state. The errors in climate-relevant aerosol properties introduced by such approximations may be large but have not yet been well quantified. This paper quantitatively addresses the question of to what extent the aerosol mixing state must be resolved to adequately represent the optical properties and cloud-nucleating properties of particle populations. Using a detailed benchmarking model to simulate gas condensation and particle coagulation, we show that, after the particles evolve in the atmosphere, simple mixing-state representations are sufficient for modeling cloud condensation nuclei concentrations, and we quantify the mixing time scale that characterizes this transformation. In contrast, a detailed representation of the mixing state is required to model aerosol light absorption, even for populations that are fully mixed with respect to their hygroscopic properties.

scholarly journals Using different assumptions of aerosol mixing state and chemical composition to predict CCN concentrations based on field measurements in urban Beijing

2018 ◽  
Vol 18 (9) ◽  
pp. 6907-6921 ◽  
Author(s):  
Jingye Ren ◽  
Fang Zhang ◽  
Yuying Wang ◽  
Don Collins ◽  
Xinxin Fan ◽  
...  
Keyword(s):  
Chemical Composition ◽  
Black Carbon ◽  
Cloud Condensation Nuclei ◽  
Field Measurements ◽  
Minor Role ◽  
Mixing State ◽  
Mixing Processes ◽  
Size Dependent ◽  
Aerosol Mixing State ◽  
Aerosol Chemical Composition

Abstract. Understanding the impacts of aerosol chemical composition and mixing state on cloud condensation nuclei (CCN) activity in polluted areas is crucial for accurately predicting CCN number concentrations (NCCN). In this study, we predict NCCN under five assumed schemes of aerosol chemical composition and mixing state based on field measurements in Beijing during the winter of 2016. Our results show that the best closure is achieved with the assumption of size dependent chemical composition for which sulfate, nitrate, secondary organic aerosols, and aged black carbon are internally mixed with each other but externally mixed with primary organic aerosol and fresh black carbon (external–internal size-resolved, abbreviated as EI–SR scheme). The resulting ratios of predicted-to-measured NCCN (RCCN_p∕m) were 0.90 – 0.98 under both clean and polluted conditions. Assumption of an internal mixture and bulk chemical composition (INT–BK scheme) shows good closure with RCCN_p∕m of 1.0 –1.16 under clean conditions, implying that it is adequate for CCN prediction in continental clean regions. On polluted days, assuming the aerosol is internally mixed and has a chemical composition that is size dependent (INT–SR scheme) achieves better closure than the INT–BK scheme due to the heterogeneity and variation in particle composition at different sizes. The improved closure achieved using the EI–SR and INT–SR assumptions highlight the importance of measuring size-resolved chemical composition for CCN predictions in polluted regions. NCCN is significantly underestimated (with RCCN_p∕m of 0.66 – 0.75) when using the schemes of external mixtures with bulk (EXT–BK scheme) or size-resolved composition (EXT–SR scheme), implying that primary particles experience rapid aging and physical mixing processes in urban Beijing. However, our results show that the aerosol mixing state plays a minor role in CCN prediction when the κorg exceeds 0.1.

scholarly journals A Stochastic Representation of Subgrid Uncertainty for Dynamical Core Development

2019 ◽  
Vol 100 (6) ◽  
pp. 1091-1101 ◽  
Author(s):  
Aneesh Subramanian ◽  
Stephan Juricke ◽  
Peter Dueben ◽  
Tim Palmer
Keyword(s):  
Climate Models ◽  
Low Cost ◽  
Weather Prediction ◽  
Development Work ◽  
Test Case ◽  
Computationally Efficient ◽  
New Paradigm ◽  
Stochastic Representation ◽  
Dynamical Core ◽  
Stochastic Framework

AbstractNumerical weather prediction and climate models comprise a) a dynamical core describing resolved parts of the climate system and b) parameterizations describing unresolved components. Development of new subgrid-scale parameterizations is particularly uncertain compared to representing resolved scales in the dynamical core. This uncertainty is currently represented by stochastic approaches in several operational weather models, which will inevitably percolate into the dynamical core. Hence, implementing dynamical cores with excessive numerical accuracy will not bring forecast gains, may even hinder them since valuable computer resources will be tied up doing insignificant computation, and therefore cannot be deployed for more useful gains, such as increasing model resolution or ensemble sizes. Here we describe a low-cost stochastic scheme that can be implemented in any existing deterministic dynamical core as an additive noise term. This scheme could be used to adjust accuracy in future dynamical core development work. We propose that such an additive stochastic noise test case should become a part of the routine testing and development of dynamical cores in a stochastic framework. The overall key point of the study is that we should not develop dynamical cores that are more precise than the level of uncertainty provided by our stochastic scheme. In this way, we present a new paradigm for dynamical core development work, ensuring that weather and climate models become more computationally efficient. We show some results based on tests done with the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) dynamical core.

scholarly journals Biomass burning aerosols in most climate models are too absorbing

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Hunter Brown ◽  
Xiaohong Liu ◽  
Rudra Pokhrel ◽  
Shane Murphy ◽  
Zheng Lu ◽  
...  
Keyword(s):  
Biomass Burning ◽  
Climate Models ◽  
Carbonaceous Aerosol ◽  
Mixing State ◽  
Aerosol Composition ◽  
Aerosol Absorption ◽  
Aerosol Effect ◽  
Community Atmosphere Model ◽  
Aerosol Effects ◽  
Aerosol Mixing State

AbstractUncertainty in the representation of biomass burning (BB) aerosol composition and optical properties in climate models contributes to a range in modeled aerosol effects on incoming solar radiation. Depending on the model, the top-of-the-atmosphere BB aerosol effect can range from cooling to warming. By relating aerosol absorption relative to extinction and carbonaceous aerosol composition from 12 observational datasets to nine state-of-the-art Earth system models/chemical transport models, we identify varying degrees of overestimation in BB aerosol absorptivity by these models. Modifications to BB aerosol refractive index, size, and mixing state improve the Community Atmosphere Model version 5 (CAM5) agreement with observations, leading to a global change in BB direct radiative effect of −0.07 W m−2, and regional changes of −2 W m−2 (Africa) and −0.5 W m−2 (South America/Temperate). Our findings suggest that current modeled BB contributes less to warming than previously thought, largely due to treatments of aerosol mixing state.

scholarly journals Mixing state and compositional effects on CCN activity and droplet growth kinetics of size-resolved CCN in an urban environment

2011 ◽  
Vol 11 (12) ◽  
pp. 32723-32768 ◽  
Author(s):  
L. T. Padró ◽  
R. H. Moore ◽  
X. Zhang ◽  
N. Rastogi ◽  
R. J. Weber ◽  
...  
Keyword(s):  
Chemical Composition ◽  
Cloud Condensation Nuclei ◽  
Inorganic Ions ◽  
Water Soluble ◽  
Anthropogenic Sources ◽  
Droplet Growth ◽  
Mixing State ◽  
Aerosol Composition ◽  
Aerosol Mixing State ◽  
Ccn Activity

Abstract. Aerosol composition and mixing state near anthropogenic sources can be highly variable and can challenge predictions of cloud condensation nuclei (CCN). We present in-situ size-resolved CCN measurements to quantify this predictive uncertainty, which were carried out during the 2008 summertime August Mini Intensive Gas and Aerosol Study (AMIGAS) campaign in Atlanta, GA. Aerosol chemical composition was measured by two particle-into-liquid samplers measuring water-soluble inorganic ions and total water-soluble organic carbon. Size-resolved CCN data were collected using the Scanning Mobility CCN Analysis (SMCA) method and were used to obtain characteristic aerosol hygroscopicity distributions, whose breadth reflects the aerosol compositional variability and mixing state. We find that knowledge of aerosol mixing state is important for accurate predictions of CCN concentrations and that the influence of an externally-mixed, non-CCN-active aerosol fraction varies with size from 31% for particle diameters less than 40 nm to 93% for accumulation mode aerosol during the day. This is likely indicative of the interactions between biogenic and anthropogenic emissions which contribute to the formation and transformation of aerosols in this heterogeneous environment. Assuming size-dependent aerosol mixing state and size-invariant chemical composition decreased the average CCN concentration overprediction from greater than 50–200% to less than 20%. CCN activity was parameterized using a single hygroscopicity parameter, κ, which averaged 0.16 ± 0.07 for 80 nm particles and exhibited considerable variability (range: 0.03–0.48) throughout the study period.

Model analysis of influences of aerosol mixing state upon its optical properties in East Asia

2013 ◽  
Vol 30 (4) ◽  
pp. 1201-1212 ◽  
Author(s):  
Xiao Han ◽  
Meigen Zhang ◽  
Lingyun Zhu ◽  
Liren Xu
Keyword(s):  
Optical Properties ◽  
East Asia ◽  
Model Analysis ◽  
Mixing State ◽  
Aerosol Mixing State

scholarly journals Secondary ice production in summer clouds over the Antarctic coast: an underappreciated process in atmospheric models

2021 ◽  
Vol 21 (2) ◽  
pp. 755-771
Author(s):  
Georgia Sotiropoulou ◽  
Étienne Vignon ◽  
Gillian Young ◽  
Hugh Morrison ◽  
Sebastian J. O'Shea ◽  
...  
Keyword(s):  
Climate Models ◽  
Weather Prediction ◽  
Critical Role ◽  
Supercooled Liquid ◽  
Weddell Sea ◽  
Radiation Budget ◽  
Atmospheric Models ◽  
Minimum Concentration ◽  
Ice Production ◽  
The Antarctic

Abstract. The correct representation of Antarctic clouds in atmospheric models is crucial for accurate projections of the future Antarctic climate. This is particularly true for summer clouds which play a critical role in the surface melting of the ice shelves in the vicinity of the Weddell Sea. The pristine atmosphere over the Antarctic coast is characterized by low concentrations of ice nucleating particles (INPs) which often result in the formation of supercooled liquid clouds. However, when ice formation occurs, the ice crystal number concentrations (ICNCs) are substantially higher than those predicted by existing primary ice nucleation parameterizations. The rime-splintering mechanism, thought to be the dominant secondary ice production (SIP) mechanism at temperatures between −8 and −3 ∘C, is also weak in the Weather and Research Forecasting model. Including a parameterization for SIP due to breakup (BR) from collisions between ice particles improves the ICNC representation in the modeled mixed-phase clouds, suggesting that BR could account for the enhanced ICNCs often found in Antarctic clouds. The model results indicate that a minimum concentration of about ∼ 0.1 L−1 of primary ice crystals is necessary and sufficient to initiate significant breakup to explain the observations, while our findings show little sensitivity to increasing INPs. The BR mechanism is currently not represented in most weather prediction and climate models; including this process can have a significant impact on the Antarctic radiation budget.

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