Iron is an essential nutrient for phytoplankton. Although iron-containing dust mobilized from arid regions supplies the majority of the iron to the oceans, the key flux in terms of the biogeochemical response to atmospheric deposition is the amount of soluble or bioavailable iron. Atmospheric processing of mineral aerosols by anthropogenic pollutants (e.g. sulfuric acid) may transform insoluble iron into soluble forms. Previous studies have suggested higher iron solubility in smaller particles, as they are subject to more thorough atmospheric processing due to a longer residence time than coarse particles. On the other hand, the specific mineralogy of iron in dust may also influence the particulate iron solubility in size. Compared to mineral dust aerosols, iron from combustion sources could be more soluble, and found more frequently in smaller particles. Internal mixing of alkaline dust with iron-containing minerals could significantly reduce iron dissolution in large dust aerosols due to the buffering effect, which may, in contrast, yield higher solubility in smaller particles externally mixed with alkaline dust (Ito and Feng, 2010). Here, we extend the modeling study of Ito and Feng (2010) to investigate atmospheric processing of mineral aerosols from African dust. In contrast to Asian dust, we used a slower dissolution rate for African dust in the fine mode. We compare simulated fractional iron solubility with observations. The inclusion of alkaline compounds in aqueous chemistry substantially limits the iron dissolution during long-range transport to the Atlantic Ocean: only a small fraction of iron (<0.2%) dissolves from illite in coarsemode dust aerosols with 0.45% soluble iron initially. In contrast, a significant fraction (1–1.5%) dissolves in fine-mode dust aerosols due to the acid mobilization of the iron-containing minerals externally mixed with carbonate minerals. Consequently, the model generally reproduces higher iron solubility in smaller particles as suggested by measurements over the Atlantic Ocean. Our results imply that the dissolution of iron in African dust is generally slower than that in Asian dust. Conventionally, dust is assumed as the major supply of bioavailable iron with a constant solubility at 1–2% to the remote ocean. Therefore, the timing and location of the atmospheric iron input to the ocean with detailed modeling of atmospheric processing could be different from those previously assumed. Past and future changes in aerosol supply of bioavailable iron might play a greater role in the nutrient supply for phytoplankton production in the upper ocean, as global warming has been predicted to intensify stratification and reduce vertical mixing from the deep ocean. Thus the feedback of climate change through ocean uptake of carbon dioxide as well as via aerosol-cloud interaction might be modified by the inclusion of iron chemistry in the atmosphere.
Abstract. In the southeast Atlantic, well-defined smoke plumes from Africa advect over marine boundary layer cloud decks; both are most extensive around September, when most of the smoke resides in the free troposphere. A framework is put forth for evaluating the performance of a range of global and regional atmospheric composition models against observations made during the NASA ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) airborne mission in September 2016. A strength of the comparison is a focus on the spatial distribution of a wider range of aerosol composition and optical properties than has been done previously. The sparse airborne observations are aggregated into approximately 2∘ grid boxes and into three vertical layers: 3–6 km, the layer from cloud top to 3 km, and the cloud-topped marine boundary layer. Simulated aerosol extensive properties suggest that the flight-day observations are reasonably representative of the regional monthly average, with systematic deviations of 30 % or less. Evaluation against observations indicates that all models have strengths and weaknesses, and there is no single model that is superior to all the others in all metrics evaluated. Whereas all six models typically place the top of the smoke layer within 0–500 m of the airborne lidar observations, the models tend to place the smoke layer bottom 300–1400 m lower than the observations. A spatial pattern emerges, in which most models underestimate the mean of most smoke quantities (black carbon, extinction, carbon monoxide) on the diagonal corridor between 16∘ S, 6∘ E, and 10∘ S, 0∘ E, in the 3–6 km layer, and overestimate them further south, closer to the coast, where less aerosol is present. Model representations of the above-cloud aerosol optical depth differ more widely. Most models overestimate the organic aerosol mass concentrations relative to those of black carbon, and with less skill, indicating model uncertainties in secondary organic aerosol processes. Regional-mean free-tropospheric model ambient single scattering albedos vary widely, between 0.83 and 0.93 compared with in situ dry measurements centered at 0.86, despite minimal impact of humidification on particulate scattering. The modeled ratios of the particulate extinction to the sum of the black carbon and organic aerosol mass concentrations (a mass extinction efficiency proxy) are typically too low and vary too little spatially, with significant inter-model differences. Most models overestimate the carbonaceous mass within the offshore boundary layer. Overall, the diversity in the model biases suggests that different model processes are responsible. The wide range of model optical properties requires further scrutiny because of their importance for radiative effect estimates.
research-article A Building Integrated Control Platform Oriented Towards Intelligent Building Share on Author: Feng Yan University of Chinese Academy of Sciences and Institute of Software, Chinese Academy of Sciences, China University of Chinese Academy of Sciences and Institute of Software, Chinese Academy of Sciences, ChinaView Profile Authors Info & Claims ICAIIS 2021: 2021 2nd International Conference on Artificial Intelligence and Information SystemsMay 2021 Article No.: 217Pages 1–10https://doi.org/10.1145/3469213.3470424Online:18 August 2021Publication History 0citation15DownloadsMetricsTotal Citations0Total Downloads15Last 12 Months15Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
The representation of aerosols in climate-chemistry models is important for air quality and climate change research, but it can require significant computational resources. To overcome this, simpler modules such as modal aerosol modules with three lognormal modes (MAM3) can be used. In this study, the coupling of the Carbon Bond Mechanism, version Z (CBMZ), and MAM3 chemistry modules in WRF-CAM5 was improved by adding biomass-burning emissions to both gas- and particle-phase chemistry and incorporating a conversion mechanism between volatile organic compounds (VOCs) and secondary organic carbons (SOCs). The study conducted six simulations over the western U.S. and northeastern Pacific region and compared the model’s performance with observational benchmarks such as reanalysis, ground-based, and satellite data. The results showed that the model with enhanced chemistry capabilities had a 31% and 58% reduction in root-mean-square errors (RMSE) for black carbon (BC) and organic carbon (OC) surface concentrations, respectively. The earlier release of the WRF-CAM5 version had two deficiencies that were addressed in this study. This research highlights the importance of accurate aerosol representation in climate-chemistry models for improving accuracy and reducing errors in simulations.
In response to the challenge presented by the wide range of scenarios and targets in intricate disinfection environments, this study introduces an improved target detection algorithm. The algorithm integrates a Coordinate Attention mechanism, thereby enhancing the ability to capture information related to small targets. Additionally, the SPPFCSPC module effectively enhances the feature extraction capabilities of the backbone feature network. To address issues related to slow convergence and imprecise regression outcomes, the Focal and Efficient IOU loss function is utilized. Experimental findings demonstrate that the proposed algorithm achieves a remarkable mean Average Precision of 91.4%, representing a notable enhancement of 2.4% over the YOLOv8n model.
Abstract We examine the temporal and the spatial trends in the concentrations of black carbon (BC) – recorded by the IMPROVE monitoring network for the past 20 years – in California. Annual average BC concentrations in California have decreased by about 50% from 0.46 μg m−3 in 1989 to 0.24 μ gm−3 in 2008 compared to the corresponding reductions in diesel BC emissions (also about 50%) from a peak of 0.013 Tg Yr−1 in 1990 to 0.006 Tg Yr−1 by 2008. We attribute the observed negative trends to the reduction in vehicular emissions due to stringent statewide regulations. Our conclusion that the reduction in diesel emissions is a primary cause of the observed BC reduction is also substantiated by a significant decrease in the ratio of BC to non-BC aerosols. The absorption efficiency of aerosols at visible wavelengths – determined from the observed scattering coefficient and the observed BC – also decreased by about 50% leading to a model-inferred negative direct radiative forcing (a cooling effect) of −1.4 W m−2 (±60%) over California.
Abstract. The U.S. Department of Energy's Energy Exascale Earth System Model (E3SM) version 2.1 builds on E3SMv2 with several changes, the most notable being the addition of the Fox-Kemper et al. (2011) mixed layer eddy parameterization. This parameterization captures the effect of finite-amplitude, mixed layer eddies as an overturning streamfunction and has the primary function of restratification. Herein, we outline the changes to the mean climate state of E3SM that were introduced by the addition of this parameterization. Overall, the presence of the submesoscale parameterization improves the fidelity of the v2.1 simulation by reducing the North Atlantic ocean surface biases present in v2, as illustrated by changes to the climatological sea surface temperature and salinity, as well as Arctic sea-ice extent. Other impacts include a slight shoaling of the mixed layer depths in the North Atlantic, as well as a small improvement to the Atlantic Meridional Overturning Circulation (AMOC). We note that the expected shoaling due to the parameterization is regionally dependent in our coupled configuration. In addition, we investigate why the parameterization and its impacts on mixed layer depth have little impact on the simulated AMOC: despite increased dense water formation in the Norwegian Sea, only a small fraction of the water formed makes its way south into the North Atlantic basin. Version 2.1 also exhibits small improvements in the atmospheric climatology, with smaller biases in many notable quantities and modes of variability.
The history of development of Battery Bimonthly(Dianchi in Chinese),the chances to develop this battery publication in the past 30 years were reviewed with 9 references.
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