Relationship between cloud condensation nuclei (CCN) concentration and aerosol optical depth in the Arctic region

Abstract. A fundamental limitation of grid-based models is their inability to resolve variability on scales smaller than a grid box. Past research has shown that significant aerosol variability exists on scales smaller than these grid-boxes, which can lead to discrepancies in simulated aerosol climate effects between high and low resolution models. This study investigates the impact of neglecting sub-grid variability in present-day global microphysical aerosol models on aerosol optical depth (AOD) and cloud condensation nuclei (CCN). We introduce a novel technique to isolate the effect of aerosol variability from other sources of model variability by varying the resolution of aerosol and trace gas fields while maintaining a constant resolution in the rest of the model. We compare WRF-Chem runs in which aerosol and gases are simulated at 80 km and again at 10 km resolutions; in both simulations the other model components, such as meteorology and dynamics, are kept at the 10 km baseline resolution. We find that AOD is underestimated by 13 % and CCN is overestimated by 27 % when aerosol and gases are simulated at 80 km resolution compared to 10 km. Processes most affected by neglecting aerosol sub-grid variability are gas-phase chemistry and aerosol uptake of water through aerosol/gas equilibrium reactions. The inherent non-linearities in these processes result in large changes in aerosol parameters when aerosol and gaseous species are artificially mixed over large spatial scales. These changes in aerosol and gas concentrations are exaggerated by convective transport, which transports these altered concentrations to altitudes where their effect is more pronounced. These results demonstrate that aerosol variability can have a large impact on simulating aerosol climate effects, even when meteorology and dynamics are held constant. Future aerosol model development should focus on accounting for the effect of sub-grid variability on these processes at global scales in order to improve model predictions of the aerosol effect on climate.

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In-situ observations of aerosol-cloud interactions in Ny-Ålesund, Svalbard, during fall 2019 and spring 2020

10.5194/egusphere-egu21-15775 ◽

2021 ◽

Author(s):

Ghislain Motos ◽

Paraskevi Georgakaki ◽

Paul Zieger ◽

Jörg Wieder ◽

Ulrike Lohmann ◽

...

Keyword(s):

Atmospheric Chemistry ◽

Cloud Condensation Nuclei ◽

Arctic Region ◽

Climate Impacts ◽

Arctic Climate ◽

The Arctic ◽

Scanning Mobility Particle Sizer ◽

Aerosol Cloud ◽

Global Rate ◽

The Arctic Region

The Arctic region suffers an extreme vulnerability to climate change, with an increase in surface air temperatures that have reached twice the global rate during several decades (McBean et al., 2005). The role of clouds, and in particular low-levels clouds and fog, in this arctic amplification by regulating the energy transport from and to space has recently gained interest among the scientific community. The NASCENT 2019-2020 campaign (Ny-&#197;lesund AeroSol Cloud ExperimeNT) based in Ny-&#197;lesund, Svalbard (79&#186; North) aimed at studying the microphysical and chemical properties of low-level clouds using measurements both at the sea level and at the Zeppelin station (475 m a.s.l.). Specifically, the susceptibility of droplet formation, which has recently been shown to be highly dependent on aerosol levels in European alpine valleys (Georgakaki et al., under review), could strongly vary between the fall to winter months, with pristine-like conditions, and the higher particle concentrations generally found in spring, known as the arctic haze. First results using a scanning mobility particle sizer (SMPS) and a cloud condensation nuclei counter (CCNC) confirmed that aerosol concentrations in the range 10 < Dpart [nm] < 500 were approximatively 4-5 times higher during the months of spring 2021 compared to those of fall 2020. In addition, we found relatively low values of the aerosol hygroscopic parameter &#954;, generally below 0.3, consistently with previous studies in the arctic region (Moore et al., 2011).&#160;Georgakaki, P., Bougiatioti, A., Wieder, J., Mignani, C., Kanji, Z. A., Henneberger, J., Hervo, M., Berne, A. and Nenes, A.: On the drivers of droplet variability in Alpine mixed-phase clouds, , 34, under review.McBean, G., Alekseev, G., Chen, D., F&#248;rland, E., Fyfe, Groisman, J., P. Y., King, R., Melling, H., Voseand, R., Whitfield, P. H.: Arctic climate: past and present. Arctic Climate Impacts Assessment (ACIA), C. Symon, L. Arris and B. Heal, Eds., Cambridge University Press, Cambridge, 21-60, 2005.Moore, R. H., Bahreini, R., Brock, C. A., Froyd, K. D., Cozic, J., Holloway, J. S., Middlebrook, A. M., Murphy, D. M. and Nenes, A.: Hygroscopicity and composition of Alaskan Arctic CCN during April 2008, Atmospheric Chemistry and Physics, 11(22), 11807&#8211;11825, https://doi.org/10.5194/acp-11-11807-2011, 2011.

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Effect of aerosol subgrid variability on aerosol optical depth and cloud condensation nuclei: implications for global aerosol modelling

Atmospheric Chemistry and Physics ◽

10.5194/acp-16-13619-2016 ◽

2016 ◽

Vol 16 (21) ◽

pp. 13619-13639 ◽

Cited By ~ 13

Author(s):

Natalie Weigum ◽

Nick Schutgens ◽

Philip Stier

Keyword(s):

Aerosol Optical Depth ◽

Optical Depth ◽

Spatial Scales ◽

Cloud Condensation Nuclei ◽

Convective Transport ◽

Condensation Nuclei ◽

Gaseous Species ◽

Climate Effects ◽

Aerosol Model ◽

The Impact

Abstract. A fundamental limitation of grid-based models is their inability to resolve variability on scales smaller than a grid box. Past research has shown that significant aerosol variability exists on scales smaller than these grid boxes, which can lead to discrepancies in simulated aerosol climate effects between high- and low-resolution models. This study investigates the impact of neglecting subgrid variability in present-day global microphysical aerosol models on aerosol optical depth (AOD) and cloud condensation nuclei (CCN). We introduce a novel technique to isolate the effect of aerosol variability from other sources of model variability by varying the resolution of aerosol and trace gas fields while maintaining a constant resolution in the rest of the model. We compare WRF-Chem (Weather and Research Forecast model) runs in which aerosol and gases are simulated at 80 km and again at 10 km resolutions; in both simulations the other model components, such as meteorology and dynamics, are kept at the 10 km baseline resolution. We find that AOD is underestimated by 13 % and CCN is overestimated by 27 % when aerosol and gases are simulated at 80 km resolution compared to 10 km. The processes most affected by neglecting aerosol subgrid variability are gas-phase chemistry and aerosol uptake of water through aerosol–gas equilibrium reactions. The inherent non-linearities in these processes result in large changes in aerosol properties when aerosol and gaseous species are artificially mixed over large spatial scales. These changes in aerosol and gas concentrations are exaggerated by convective transport, which transports these altered concentrations to altitudes where their effect is more pronounced. These results demonstrate that aerosol variability can have a large impact on simulating aerosol climate effects, even when meteorology and dynamics are held constant. Future aerosol model development should focus on accounting for the effect of subgrid variability on these processes at global scales in order to improve model predictions of the aerosol effect on climate.

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Multiscale Applications of Two Online-Coupled Meteorology-Chemistry Models during Recent Field Campaigns in Australia, Part I: Model Description and WRF/Chem-ROMS Evaluation Using Surface and Satellite Data and Sensitivity to Spatial Grid Resolutions

Atmosphere ◽

10.3390/atmos10040189 ◽

2019 ◽

Vol 10 (4) ◽

pp. 189 ◽

Cited By ~ 6

Author(s):

Yang Zhang ◽

Chinmay Jena ◽

Kai Wang ◽

Clare Paton-Walsh ◽

Élise-Andrée Guérette ◽

...

Keyword(s):

Boundary Layer ◽

Wind Speed ◽

Aerosol Optical Depth ◽

Optical Depth ◽

Cloud Condensation Nuclei ◽

Cloud Microphysics ◽

Condensation Nuclei ◽

Field Campaigns ◽

Spatial Grid ◽

Pm2.5 And Pm10

Air pollution and associated human exposure are important research areas in Greater Sydney, Australia. Several field campaigns were conducted to characterize the pollution sources and their impacts on ambient air quality including the Sydney Particle Study Stages 1 and 2 (SPS1 and SPS2), and the Measurements of Urban, Marine, and Biogenic Air (MUMBA). In this work, the Weather Research and Forecasting model with chemistry (WRF/Chem) and the coupled WRF/Chem with the Regional Ocean Model System (ROMS) (WRF/Chem-ROMS) are applied during these field campaigns to assess the models’ capability in reproducing atmospheric observations. The model simulations are performed over quadruple-nested domains at grid resolutions of 81-, 27-, 9-, and 3-km over Australia, an area in southeastern Australia, an area in New South Wales, and the Greater Sydney area, respectively. A comprehensive model evaluation is conducted using surface observations from these field campaigns, satellite retrievals, and other data. This paper evaluates the performance of WRF/Chem-ROMS and its sensitivity to spatial grid resolutions. The model generally performs well at 3-, 9-, and 27-km resolutions for sea-surface temperature and boundary layer meteorology in terms of performance statistics, seasonality, and daily variation. Moderate biases occur for temperature at 2-m and wind speed at 10-m in the mornings and evenings due to the inaccurate representation of the nocturnal boundary layer and surface heat fluxes. Larger underpredictions occur for total precipitation due to the limitations of the cloud microphysics scheme or cumulus parameterization. The model performs well at 3-, 9-, and 27-km resolutions for surface O3 in terms of statistics, spatial distributions, and diurnal and daily variations. The model underpredicts PM2.5 and PM10 during SPS1 and MUMBA but overpredicts PM2.5 and underpredicts PM10 during SPS2. These biases are attributed to inaccurate meteorology, precursor emissions, insufficient SO2 conversion to sulfate, inadequate dispersion at finer grid resolutions, and underprediction in secondary organic aerosol. The model gives moderate biases for net shortwave radiation and cloud condensation nuclei but large biases for other radiative and cloud variables. The performance of aerosol optical depth and latent/sensible heat flux varies for different simulation periods. Among all variables evaluated, wind speed at 10-m, precipitation, surface concentrations of CO, NO, NO2, SO2, O3, PM2.5, and PM10, aerosol optical depth, cloud optical thickness, cloud condensation nuclei, and column NO2 show moderate-to-strong sensitivity to spatial grid resolutions. The use of finer grid resolutions (3- or 9-km) can generally improve the performance for those variables. While the performance for most of these variables is consistent with that over the U.S. and East Asia, several differences along with future work are identified to pinpoint reasons for such differences.

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Nuclear icebreaker fleet and power supply on the basis of floating power units

Voprosy regionalnoj ekonomiki ◽

10.21499/2078-4023-2018--4-110-113 ◽

2018 ◽

Vol 35 (4) ◽

pp. 110-113

Author(s):

V. A. Tupchienko ◽

H. G. Imanova

Keyword(s):

Foreign Policy ◽

Power Supply ◽

Nuclear Power ◽

Arctic Region ◽

Nuclear Industry ◽

The Arctic ◽

Far North ◽

The Arctic Region

The article deals with the problem of the development of the domestic nuclear icebreaker fleet in the context of the implementation of nuclear logistics in the Arctic. The paper analyzes the key achievements of the Russian nuclear industry, highlights the key areas of development of the nuclear sector in the Far North, and identifies aspects of the development of mechanisms to ensure access to energy on the basis of floating nuclear power units. It is found that Russia is currently a leader in the implementation of the nuclear aspect of foreign policy and in providing energy to the Arctic region.

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