scholarly journals Exploring the Change in PM2.5 and Ozone Concentrations Caused by Aerosol–Radiation Interactions and Aerosol–Cloud Interactions and the Relationship with Meteorological Factors

Atmosphere ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1585
Author(s):  
Xin Zhang ◽  
Chengduo Yuan ◽  
Zibo Zhuang
Keyword(s):  
Ozone Concentration ◽  
Coupled Model ◽  
Correlation Coefficients ◽  
Northern China ◽  
Hygroscopic Growth ◽  
Oxidation Rates ◽  
Aerosol Cloud ◽  
Ozone Concentrations ◽  
Aerosol Cloud Interactions ◽  
Pm2.5 Concentration

Aerosols can interact with other meteorological variables in the air via aerosol–radiation or aerosol–cloud interactions (ARIs/ACIs), thus affecting the concentrations of particle pollutants and ozone. The online-coupled model WRF-Chem was applied to simulate the changes in the PM2.5 (particulate matter less than 2.5 μm in aerodynamic diameter) and ozone concentrations that are caused by these mechanisms in China by conducting three parallel sensitivity tests. In each case, availabilities of aerosol–radiation interactions and aerosol–cloud interactions were set differently in order to distinguish each pathway. Partial correlation coefficients were also analyzed using statistical tools. As suggested by the results, the ARIs reduced ground air temperature, wind speed, and planetary boundary height while increasing relative humidity in most places. Consequently, the ozone concentration in the corresponding region declined by 4%, with a rise in the local annual mean PM2.5 concentration by approximately 12 μm/m3. The positive feedback of the PM2.5 concentration via ACIs was also found in some city clusters across China, despite the overall enhancement value via ACIs being merely around a quarter to half that via ARIs. The change in ozone concentration via ACIs exhibited different trends. The ozone concentration level increased via ACIs, which can be attributed to the drier air in the south and the diminished solar radiation that is received in central and northern China. The correlation coefficient suggests that the suppression in the planetary boundary layer is the most significant factor for the increase in PM2.5 followed by the rise in moisture required for hygroscopic growth. Ozone showed a significant correlation with NO2, while oxidation rates and radiation variance were also shown to be vitally important.

scholarly journals Added Value of Aerosol-Cloud Interactions for Representing Aerosol Optical Depth in an Online Coupled Climate-Chemistry Model over Europe

Atmosphere ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 360 ◽  
Author(s):  
Laura Palacios-Peña ◽  
Juan P. Montávez ◽  
José M. López-Romero ◽  
Sonia Jerez ◽  
Juan J. Gómez-Navarro ◽  
...  
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Chemical Species ◽  
Correlation Coefficients ◽  
Horizontal Resolution ◽  
Added Value ◽  
Computational Time ◽  
Climate Modelling ◽  
Aerosol Cloud ◽  
Aerosol Cloud Interactions

Aerosol-cloud interactions (ACI) represent one of the most important sources of uncertainties in climate modelling. In this sense, realistic simulations of ACI are needed for a better understanding of the complex interactions between air pollution and the climate system. This work quantifies the added value of including ACI in an online coupled climate/chemistry model (WRF-Chem, 0.44 ∘ horizontal resolution, years 2003 to 2010) in order to assess whether there is an improvement in the representation of aerosol optical depth (AOD). Modelling results for each species have been evaluated against the Copernicus Atmosphere Monitoring Service (CAMS) reanalysis, and AOD at 675 nm has been compared to AERONET data. Results indicate that the improvements of the monthly biases are around 8% for total AOD550 when including ACI, reaching 20% for the monthly bias in AOD550 coming from dust. Moreover, the temporal representation of AOD550 largely improves (increase in the Pearson time correlation coefficients), ranging from 6% to 20% depending on the chemical species considered. The benefits from this improvement overcome the problems derived from the high computational time required in ACI simulations (eight times higher with respect to simulations not including aerosol-cloud interactions).

scholarly journals Spatiotemporal variations of tropospheric ozone concentrations in the Warsaw Agglomeration (Poland)

2014 ◽  
Vol 46 (3) ◽  
pp. 247-261 ◽  
Author(s):  
Katarzyna Rozbicka ◽  
Tomasz Rozbicki
Keyword(s):  
Ozone Concentration ◽  
Tropospheric Ozone ◽  
Correlation Coefficients ◽  
Strong Relationship ◽  
Urban Site ◽  
Regression Equations ◽  
Spatiotemporal Variations ◽  
Ozone Concentrations ◽  
High Level ◽  
Autumn And Winter

Abstract Spatiotemporal variations of tropospheric ozone concentrations in the Warsaw Agglomeration (Poland). The study uses ozone concentrations from stations in Warsaw Agglomeration and its vicinity. Diversity of localizations of considered station, in terms of type of emissivity, allows on comparison of air pollution conditions by ozone in Warsaw area. Concentration of ozone in summer and spring were above twice greater than the concentration in autumn and winter. The greatest differences between weekend days concentration and work days concentration occur during autumn and winter, but in the same time the differences during the day are the least, especially in urban site stations. Statistics analysis shows strong relationship between ozone concentration and nitrogen dioxide concentration and meteorological elements especially for days with high level of ozone concentration. For these days regression equations were statistically significant (α = 1%) and correlation coefficients were greater than 0.81. Polynomial of IV power is the best fitted function of one-hourly values of ozone concentration course in particular seasons.

scholarly journals Spatial–Temporal Distribution Variation of Ground-Level Ozone in China’s Pearl River Delta Metropolitan Region

2021 ◽  
Vol 18 (3) ◽  
pp. 872
Author(s):  
An Zhang ◽  
Jinhuang Lin ◽  
Wenhui Chen ◽  
Mingshui Lin ◽  
Chengcheng Lei
Keyword(s):  
Ozone Concentration ◽  
Pearl River Delta ◽  
Temporal Distribution ◽  
Ground Level ◽  
Metropolitan Region ◽  
Pearl River ◽  
Ozone Pollution ◽  
Ground Level Ozone ◽  
Ozone Concentrations ◽  
The North

Long-term exposure to ozone pollution will cause severe threats to residents’ physical and mental health. Ground-level ozone is the most severe air pollutant in China’s Pearl River Delta Metropolitan Region (PRD). It is of great significance to accurately reveal the spatial–temporal distribution characteristics of ozone pollution exposure patterns. We used the daily maximum 8-h ozone concentration data from PRD’s 55 air quality monitoring stations in 2015 as input data. We used six models of STK and ordinary kriging (OK) for the simulation of ozone concentration. Then we chose a better ozone pollution prediction model to reveal the ozone exposure characteristics of the PRD in 2015. The results show that the Bilonick model (BM) model had the highest simulation precision for ozone in the six models for spatial–temporal kriging (STK) interpolation, and the STK model’s simulation prediction results are significantly better than the OK model. The annual average ozone concentrations in the PRD during 2015 showed a high spatial variation in the north and east and low in the south and west. Ozone concentrations were relatively high in summer and autumn and low in winter and spring. The center of gravity of ozone concentrations tended to migrate to the north and west before moving to the south and then finally migrating to the east. The ozone’s spatial autocorrelation was significant and showed a significant positive correlation, mainly showing high-high clustering and low-low clustering. The type of clustering undergoes temporal migration and conversion over the four seasons, with spatial autocorrelation during winter the most significant.

scholarly journals Significant underestimation of radiative forcing by aerosol–cloud interactions derived from satellite-based methods

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Hailing Jia ◽  
Xiaoyan Ma ◽  
Fangqun Yu ◽  
Johannes Quaas
Keyword(s):  
Radiative Forcing ◽  
Cloud Fraction ◽  
Future Climate Change ◽  
Satellite Measurements ◽  
Aerosol Cloud ◽  
Fine Mode ◽  
Sampling Biases ◽  
Aerosol Cloud Interactions ◽  
Significant Underestimation ◽  
Weak Satellite

AbstractSatellite-based estimates of radiative forcing by aerosol–cloud interactions (RFaci) are consistently smaller than those from global models, hampering accurate projections of future climate change. Here we show that the discrepancy can be substantially reduced by correcting sampling biases induced by inherent limitations of satellite measurements, which tend to artificially discard the clouds with high cloud fraction. Those missed clouds exert a stronger cooling effect, and are more sensitive to aerosol perturbations. By accounting for the sampling biases, the magnitude of RFaci (from −0.38 to −0.59 W m−2) increases by 55 % globally (133 % over land and 33 % over ocean). Notably, the RFaci further increases to −1.09 W m−2 when switching total aerosol optical depth (AOD) to fine-mode AOD that is a better proxy for CCN than AOD. In contrast to previous weak satellite-based RFaci, the improved one substantially increases (especially over land), resolving a major difference with models.

scholarly journals 100 Years of Progress in Cloud Physics, Aerosols, and Aerosol Chemistry Research

2019 ◽  
Vol 59 ◽  
pp. 11.1-11.72 ◽  
Author(s):  
Sonia M. Kreidenweis ◽  
Markus Petters ◽  
Ulrike Lohmann
Keyword(s):  
Radiative Forcing ◽  
Cloud Microphysics ◽  
Climate System ◽  
Mitigation Strategies ◽  
Climate Projections ◽  
Weather Modification ◽  
Theoretical Understanding ◽  
Aerosol Cloud ◽  
Chemistry Research ◽  
Aerosol Cloud Interactions

Abstract This chapter reviews the history of the discovery of cloud nuclei and their impacts on cloud microphysics and the climate system. Pioneers including John Aitken, Sir John Mason, Hilding Köhler, Christian Junge, Sean Twomey, and Kenneth Whitby laid the foundations of the field. Through their contributions and those of many others, rapid progress has been made in the last 100 years in understanding the sources, evolution, and composition of the atmospheric aerosol, the interactions of particles with atmospheric water vapor, and cloud microphysical processes. Major breakthroughs in measurement capabilities and in theoretical understanding have elucidated the characteristics of cloud condensation nuclei and ice nucleating particles and the role these play in shaping cloud microphysical properties and the formation of precipitation. Despite these advances, not all their impacts on cloud formation and evolution have been resolved. The resulting radiative forcing on the climate system due to aerosol–cloud interactions remains an unacceptably large uncertainty in future climate projections. Process-level understanding of aerosol–cloud interactions remains insufficient to support technological mitigation strategies such as intentional weather modification or geoengineering to accelerating Earth-system-wide changes in temperature and weather patterns.

scholarly journals Ocean Emission Effects on Aerosol-Cloud Interactions: Insights from Two Case Studies

2010 ◽  
Vol 2010 ◽  
pp. 1-9 ◽  
Author(s):  
Armin Sorooshian ◽  
Hanh T. Duong
Keyword(s):  
Case Studies ◽  
Field Experiments ◽  
Remote Sensing Data ◽  
Anthropogenic Pollution ◽  
Cloud Microphysics ◽  
Marine Aerosols ◽  
Aerosol Cloud ◽  
First Case ◽  
Wide Range ◽  
Aerosol Cloud Interactions

Two case studies are discussed that evaluate the effect of ocean emissions on aerosol-cloud interactions. A review of the first case study from the eastern Pacific Ocean shows that simultaneous aircraft and space-borne observations are valuable in detecting links between ocean biota emissions and marine aerosols, but that the effect of the former on cloud microphysics is less clear owing to interference from background anthropogenic pollution and the difficulty with field experiments in obtaining a wide range of aerosol conditions to robustly quantify ocean effects on aerosol-cloud interactions. To address these limitations, a second case was investigated using remote sensing data over the less polluted Southern Ocean region. The results indicate that cloud drop size is reduced more for a fixed increase in aerosol particles during periods of higher ocean chlorophyll A. Potential biases in the results owing to statistical issues in the data analysis are discussed.

scholarly journals Evaluation of modeled aerosol-cloud interactions using data from the ORACLES and LASIC field campaigns

2020 ◽  
Author(s):  
Calvin Howes ◽  
Pablo Saide ◽  
Paquita Zuidema ◽  
Jianhao Zhang ◽  
Michael Diamond ◽  
...  
Keyword(s):  
Aerosol Cloud ◽  
Field Campaigns ◽  
Using Data ◽  
Aerosol Cloud Interactions

Evaluation of Scalar Advection Schemes in the Advanced Research WRF Model Using Large-Eddy Simulations of Aerosol–Cloud Interactions

2009 ◽  
Vol 137 (8) ◽  
pp. 2547-2558 ◽  
Author(s):  
Hailong Wang ◽  
William C. Skamarock ◽  
Graham Feingold
Keyword(s):  
Large Eddy Simulations ◽  
Numerical Dispersion ◽  
Integration Scheme ◽  
Cloud Albedo ◽  
Flux Limiter ◽  
Aerosol Cloud ◽  
Advection Schemes ◽  
Large Eddy ◽  
Runge Kutta ◽  
Aerosol Cloud Interactions

Abstract In the Advanced Research Weather Research and Forecasting Model (ARW), versions 3.0 and earlier, advection of scalars was performed using the Runge–Kutta time-integration scheme with an option of using a positive-definite (PD) flux limiter. Large-eddy simulations of aerosol–cloud interactions using the ARW model are performed to evaluate the advection schemes. The basic Runge–Kutta scheme alone produces spurious oscillations and negative values in scalar mixing ratios because of numerical dispersion errors. The PD flux limiter assures positive definiteness but retains the oscillations with an amplification of local maxima by up to 20% in the tests. These numerical dispersion errors contaminate active scalars directly through the advection process and indirectly through physical and dynamical feedbacks, leading to a misrepresentation of cloud physical and dynamical processes. A monotonic flux limiter is introduced to correct the generally accurate but dispersive solutions given by high-order Runge–Kutta scheme. The monotonic limiter effectively minimizes the dispersion errors with little significant enhancement of numerical diffusion errors. The improvement in scalar advection using the monotonic limiter is discussed in the context of how the different advection schemes impact the quantification of aerosol–cloud interactions. The PD limiter results in 20% (10%) fewer cloud droplets and 22% (5%) smaller cloud albedo than the monotonic limiter under clean (polluted) conditions. Underprediction of cloud droplet number concentration by the PD limiter tends to trigger the early formation of precipitation in the clean case, leading to a potentially large impact on cloud albedo change.

scholarly journals The Dynamical Core, Physical Parameterizations, and Basic Simulation Characteristics of the Atmospheric Component AM3 of the GFDL Global Coupled Model CM3

2011 ◽  
Vol 24 (13) ◽  
pp. 3484-3519 ◽  
Author(s):  
Leo J. Donner ◽  
Bruce L. Wyman ◽  
Richard S. Hemler ◽  
Larry W. Horowitz ◽  
Yi Ming ◽  
...  
Keyword(s):  
Twentieth Century ◽  
Sea Ice ◽  
System Component ◽  
Dynamical Core ◽  
Atmospheric Component ◽  
Clear Sky ◽  
Aerosol Cloud ◽  
Droplet Activation ◽  
Aerosol Cloud Interactions ◽  
Physical Parameterizations

Abstract The Geophysical Fluid Dynamics Laboratory (GFDL) has developed a coupled general circulation model (CM3) for the atmosphere, oceans, land, and sea ice. The goal of CM3 is to address emerging issues in climate change, including aerosol–cloud interactions, chemistry–climate interactions, and coupling between the troposphere and stratosphere. The model is also designed to serve as the physical system component of earth system models and models for decadal prediction in the near-term future—for example, through improved simulations in tropical land precipitation relative to earlier-generation GFDL models. This paper describes the dynamical core, physical parameterizations, and basic simulation characteristics of the atmospheric component (AM3) of this model. Relative to GFDL AM2, AM3 includes new treatments of deep and shallow cumulus convection, cloud droplet activation by aerosols, subgrid variability of stratiform vertical velocities for droplet activation, and atmospheric chemistry driven by emissions with advective, convective, and turbulent transport. AM3 employs a cubed-sphere implementation of a finite-volume dynamical core and is coupled to LM3, a new land model with ecosystem dynamics and hydrology. Its horizontal resolution is approximately 200 km, and its vertical resolution ranges approximately from 70 m near the earth’s surface to 1 to 1.5 km near the tropopause and 3 to 4 km in much of the stratosphere. Most basic circulation features in AM3 are simulated as realistically, or more so, as in AM2. In particular, dry biases have been reduced over South America. In coupled mode, the simulation of Arctic sea ice concentration has improved. AM3 aerosol optical depths, scattering properties, and surface clear-sky downward shortwave radiation are more realistic than in AM2. The simulation of marine stratocumulus decks remains problematic, as in AM2. The most intense 0.2% of precipitation rates occur less frequently in AM3 than observed. The last two decades of the twentieth century warm in CM3 by 0.32°C relative to 1881–1920. The Climate Research Unit (CRU) and Goddard Institute for Space Studies analyses of observations show warming of 0.56° and 0.52°C, respectively, over this period. CM3 includes anthropogenic cooling by aerosol–cloud interactions, and its warming by the late twentieth century is somewhat less realistic than in CM2.1, which warmed 0.66°C but did not include aerosol–cloud interactions. The improved simulation of the direct aerosol effect (apparent in surface clear-sky downward radiation) in CM3 evidently acts in concert with its simulation of cloud–aerosol interactions to limit greenhouse gas warming.

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