Skip to content. Among Madmen. Among Madmen Book Review:. Adventures Among Madmen Madwomen. Penal Philosophy. Penal Philosophy Book Review:. From Madman to Crime Fighter. Author : Roslynn D. Master Olof. Master Olof Book Review:. Works Book Review:. The similarity in global dust emission between the MB95 and GUST schemes is a result of the scaling with the topographic source mask.
Nevertheless, differences in the magnitude of dust emission are evident, in particular in the Middle East, central Asia, and Australia. Neither the S04 nor K14 scheme uses a preferential source function besides the binary treatment explained in Sect. Hence, dust emissions are independent of this source function and differences to other experiments are more pronounced.
Dust emissions in Asia extend over a larger area in the S04 and K14 runs and tend to be larger in North and South America. The S04 run shows decreased dust emissions in the eastern Sahara, whereas north African and Middle Eastern dust emissions are relatively homogeneous in the K14 run.
Overall, the S04 scheme produced substantially more dust emission and deposition than the other schemes. The coarser particles in the S04 experiment have only a small contribution to DOD but constitute a large amount of the emitted and deposited dust mass. In comparison, deposition and dust loading are more intense in northwestern Africa and the Middle East in the S04 scheme, and more homogeneous in the K14 scheme.
Figure A1 shows the percent contribution of dust emission and deposition at each location to their respective global and annual totals to investigate differences between the four experiments independent from the overall flux magnitudes. The relative emission deposition confirms the differences highlighted before: the spatial patterns of the MB95 and GUST are similar due to the use of the preferential source function. In contrast, the S04 experiment produced relatively more dust in northwestern Africa, while the K14 scheme generated relatively homogeneous patterns across northern Africa and the Middle East.
Kok et al. MONARCH contributed to this study with a preliminary model version, which did not yet include all developments presented here. Our new results Table A1 show that, compared to the results from Kok et al. However, besides the overestimation of North African sources, our model results agree with many of the aspects highlighted by Kok et al. Differences across configurations demonstrate the benefit of having multiple dust emission options available in one model and will help to identify aspects of parameterizing dust emission that need particular attention in future research.
The global annual average of DOD is 0. Our objective here is to evaluate the overall behavior of MONARCH across dust emission schemes rather than that of each individual scheme. The model results were obtained for DOD coarse averaged across the four model experiments. The seasonal averages were calculated with respect to the number of valid values per grid cell in the respective products.
Area-weighted root mean square error RMSE and uncentered Pearson product-moment coefficient of linear correlation r between model and observations are indicated in the respective panels. The FoO was calculated with respect to the number of days in the season.
Area-weighted RMSE and uncentered Pearson product-moment coefficient of linear correlation r are indicated in the respective panels. Pearson correlation coefficients r and RMSE are given in the figure for both the experimental average as well as the individual runs. Records which do not meet the AE criteria are less likely to be associated with dust and are shown as open circles.
As in Ginoux et al. Actual overpass times vary and may deviate slightly from this nominal time. Other polar satellite overpasses can be implemented easily.
The clear-sky DOD is currently diagnosed at the model output times in contrast to the satellite overpass times available for the all-sky DOD. We chose 1. This may be related to the pronounced topographic features in the area, which are difficult to resemble at coarse model resolution.
This may lead to an overestimation of DOD in areas in which dust is not the dominant aerosol type and where other aerosols are present. Seasonal averages of modeled all-sky co-located DOD coarse for each individual model run are shown in Fig. Differences in the modeled all-sky co-located and clear-sky DOD coarse underline the impact of the time, location, and number of missing values on the average DOD coarse. The clear-sky DOD coarse tends to be somewhat smaller compared to the all-sky co-located DOD coarse , indicating a discrepancy between modeled and observed clouds, in combination with differences in the underlying DOD.
Other areas show very similar results between the all-sky co-located and clear-sky model results. The modeled seasonal all-sky co-located FoO for each individual model run is shown in Fig. With 0. The DOD range across the four experiments is relatively similar throughout the year with the spread being the largest during the Northern Hemisphere peak dust season in March. The MB95 experiment contributes the largest DOD in January until March and the lowest during much of the remaining year, whereas the K14 experiment shows opposite behavior.
The G01 and S04 experiments are intermediate between the other two runs and best resemble the monthly global DOD for the given configurations. Whereas the results from the four experiments are very similar from November until April, the variability increases during the other months with the largest range in September. The results for the four individual runs are qualitatively similar to those for DOD, with the MB95 and K14 experiments providing, respectively, the lower and upper frames from approximately April until November, and the G01 and S04 runs being intermediate.
Global average values are indicated in each panel. Shown are the relative contributions per bin, normalized with the absolute value of the DRE for all bins left and the DRE for all particle sizes right. The diameter ranges and effective radii of each bin are given in, respectively, Sect. AOD data are computed for three data quality levels: level 1. The products from inverting sky radiance measurements are the aerosol size distribution, single scattering albedo, refractive index, effective radius, and asymmetry factor.
Almost pure mineral dust is difficult to find, except in specific areas close to desert dust sources. Instead, dust is often mixed in variable percentages with other aerosols. To isolate the atmospheric dust burden and estimate the DOD, two approaches are typically used. The first approach aims to identify records in which the measured aerosol is dominated by mineral dust based on AE.
AE is in general inversely related to the average size of the airborne particles and can be used to distinguish species with large particles like dust and sea salt. As a rule of thumb, a larger AE indicates smaller particle size. AE is typically in the range 0—4, where the upper limit corresponds to molecular extinction, and the lower limit corresponds to coarse-mode aerosols sea salt and mineral dust , indicating no wavelength dependence of AOD O'Neill et al.
According to previous studies Dubovik et al. An AE lower than 0. In addition, Fig. The station locations are shown in Fig. The time series demonstrate an overall good agreement between the average modeled and observed DOD where the temporal variability is mostly reproduced with discrepancies for individual DOD peaks. The Pearson correlation coefficients for all other stations range between around 0.
Taking into account the entire station list Appendix C , the correlation is 0. For total DOD, the correlation remains fairly constant when comparing daily and monthly instead of 3-hourly values. The RMSE decreases slightly with an increasing averaging period as then discrepancies for individual peaks become less relevant.
A similar behavior is found for DOD coarse , but with a slightly more pronounced increase also of the correlation. This strong sensitivity to the presence of dust is a result of the low atmospheric moisture content in this area. High near-surface atmospheric temperatures enhance the longwave downwelling radiation Miller et al. At the TOA, the shortwave DRE is slightly negative in most areas but slightly positive in some of the northern African dust sources related to the relatively bright underlying desert surface.
We note that the mineralogy-based set of refractive indices used in this work describes a more scattering dust in the shortwave with respect to other widely used prescriptions e. In contrast, Balkanski et al. Note that Balkanski et al. Moreover, Miller et al. On the other hand, Miller et al. MONARCH contains multiple state-of-the-art options to represent dust emissions on global and regional scales, ranging from more simplified to more complex parameterizations based on physical processes.
We tested and evaluated a set of four global model configurations for the year Differences in modeled dust emissions between the four configurations were mainly driven by the dust source description use of a preferential source mask or not and the particle-size distributions at emission.
The smaller range of simulated load among experiments is due to the shorter lifetime of the coarse particles included in the S04 scheme that exhibits larger emission. The multifaceted options of MONARCH and its dust component, combined with an advanced workflow management for use in high-performance computing environments, makes it a powerful and versatile tool applicable for process studies, operational forecasting, and climate research.
Dust ensemble runs can be generated with MONARCH by utilizing the diverse model configurations and by perturbing model parameters related to, for example, surface winds, soil humidity, and the spatial distribution of dust emission, which are deemed to be uncertain. In Di Tomaso et al. In Escribano et al. Combined meteorology and emission perturbations were shown to be necessary to produce sufficient ensemble spread in dust aerosol outflow regions Rubin et al.
This can be achieved using different meteorological fields as initial and boundary conditions in the meteorological driver of MONARCH NMMB for each forecast run in the ensemble, in addition to the dust perturbations.
In the dust reanalysis currently in production at the BSC, we use an ensemble based on stochastic perturbations of emission parameters, in conjunction with emission schemes with different physics and different meteorological initial and boundary conditions Di Tomaso et al.
Airborne dust is not a homogeneous entity but a mixture of minerals, the relative amounts of which depend on the source region. Mineralogy affects a variety of dust-related impacts, e. The capability to explicitly represent dust composition was recently added to MONARCH allowing the tagging of up to 12 different minerals.
The combination of different vegetation input data sets, drag partition approaches, and the source tagging capability allows us to represent the seasonal vegetation dynamics and provides an ideal basis to investigate the importance of dust from anthropogenic agricultural sources, for which a key driver is the seasonal vegetation growth and decay.
The benefit of online estimates within a modeling framework is that not only the emission but also the transport, deposition, and effect of anthropogenic dust can be investigated Klose et al. Figure A1 shows the percent contribution of dust emission and deposition at each location to their respective global and annual totals to visualize regional differences between the different experiments independent from the overall emission deposition magnitudes obtained.
Table A1 lists the relative contributions from different source regions to global annual dust emissions are summarized in Table A1. The dust source definitions are as in Kok et al. Table A1 Percentage contributions of different dust source regions definitions as in Kok et al. Figure A1 Percent contribution of dust emission left column and deposition right column to their respective global and annual totals for the four experiments described in Sect.
The seasonal averages were calculated with respect to the number of valid values per grid cell in the MODIS product.
They cover the main dust source regions around the globe. The intention of using only a subset of all stations is to increase confidence in that aerosol detected by AERONET photometers is predominantly dust.
Results are visualized in Fig. Download XLSX. MK implemented most of the dust-module upgrades, ran, analyzed, and evaluated the simulations, and wrote the manuscript. SB contributed to the model evaluation. In addition, CPGP implemented part of the model upgrades and contributed to manuscript writing.
All authors commented on the manuscript. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Martina Klose has received funding through the Helmholtz Association's Initiative and Networking Fund grant agreement no.
Jeronimo Escribano and Matthew L. Ron L. Jasper F. This paper was edited by Axel Lauer and reviewed by Yves Balkanski and one anonymous referee. Anderson, T. Ansmann, A. Badia, A. Balkanski, Y. Barreto, A. Basart, S. Belly, P. Does not generate ozone or harmful byproducts. HEPA filter works passively. It requires a fan to draw in air in order to trap air contaminants. Bacteria and viruses trapped in the filter can breed and reproduce. Table 1. There are many different technologies that are used in air purifiers with HEPA style filtration, such as activated carbon, pre-filters, ultraviolet light, and ionizers.
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Ionizer: Releases anions that help remove airborne particles like floating dust, bacteria, viruses and smoke. Fig 4. Heavier particles like pet dander and human hair are removed by this filter.
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Produce of grain for bread from the earth , and stones of fire from under it , 5. Speaks of precious stones and gold dust , 6. An apostolic- prophetic -messianic teaching Dr. Gunter Rappl To release supernatural signs and wonders angels love to throw gold dust or other dusts of precious stones , like diamonds, on the saints [Jeanie Jones, Signs and Wonders Speake of precious stones and gold dust , 6. And of the industry and successful Attempts of men in mining and other operations
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