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The source of dust in the global atmosphere is an important factor to better understand the role of dust aerosols in the climate system. However, it is a difficult task to attribute the airborne dust over the remote land and ocean regions to their origins since dust from various sources are mixed during long‐range transport. Recently, a multi‐model experiment, namely the AeroCom‐III Dust Source Attribution (DUSA), has been conducted to estimate the relative contribution of dust in various locations from different sources with tagged simulations from seven participating global models. The BASE run and a series of runs with nine tagged regions were made to estimate the contribution of dust emitted in East‐ and West‐Africa, Middle East, Central‐ and East‐Asia, North America, the Southern Hemisphere, and the prominent dust hot spots of the Bodélé and Taklimakan Deserts. The models generally agree in large scale mean dust distributions, however models show large diversity in dust source attribution. The inter‐model differences are significant with the global model dust diversity in 30%–50%, but the differences in regional and seasonal scales are even larger. The multi‐model analysis estimates that North Africa contributes 60% of global atmospheric dust loading, followed by Middle East and Central Asia sources (24%). Southern hemispheric sources account for 10% of global dust loading, however it contributes more than 70% of dust over the Southern Hemisphere. The study provides quantitative estimates of the impact of dust emitted from different source regions on the globe and various receptor regions including remote land, ocean, and the polar regions synthesized from the seven models. Plain Language Summary: As the most abundant aerosol type in the Earth's atmosphere, mineral dust plays an important role in global climate by interacting with incoming and outgoing radiation, providing liquid and ice cloud nuclei, and affecting atmospheric stability. The global dust sources are relatively well characterized by the remote sensing and modeling studies as the majority of dust is emitted from the so‐called dust belt which expands from North Africa to East Asia. However, it is challenging to attribute dust sources over the remote land and ocean regions, since dust is mixed during long‐range transport, where it experiences complex atmospheric processes, including horizontal and vertical‐advection, wet deposition, and dry deposition. Using the multi‐model simulations in the Aerocom/Dust Source Attribution experiment, the present study (a) examines the model diversity in dust source attribution and (b) estimates the contribution of dust sources to various receptor regions, including remote land/ocean and the polar regions in different altitudes, from the multi‐model statistics. Beyond dust sources, many remote land, ocean, and polar regions are affected by a mixture of dust from various sources around the globe. Key Points: Contributions of various dust sources are quantitatively estimated in a multi‐model experimentContributions of various sources have different horizontal and vertical distributions and seasonalityDust near source regions are dominated by dust emitted in the upwind source regions; however many remote land, ocean, and polar regions are affected by a mixture of dust from various sources around the globe [ABSTRACT FROM AUTHOR]

The uncertainty of hydrological forecasts is strongly related to the uncertainty of the rainfall field due to the nonlinear relationship between the spatio-temporal pattern of rainfall and runoff. Rain gauges are typically considered to provide reference data to rebuild precipitation fields. However, due to the density and the distribution variability of the rain gauge network, the rebuilding of the precipitation field can be affected by severe errors which compromise the hydrological simulation output. On the other hand, retrievals obtained from remote sensing observations provide spatially resolved precipitation fields, improving their representativeness. In this regard, the comparison between simulated and observed river flow discharge is crucial for assessing the effectiveness of merged precipitation data in enhancing the model's performance and its ability to realistically simulate hydrological processes. This paper aims to investigate the hydrological impact of using the merged rainfall fields from the Italian rain gauge network and the NASA Global Precipitation Measurement (GPM) IMERG precipitation product. One aspect is to highlight the benefits of applying the cellular automata algorithm to pre-process input data in order to merge them and reconstruct an improved version of the precipitation field. The cellular automata approach is evaluated in the Tanaro River basin, one of the tributaries of the Po River in Italy. As this site is characterized by the coexistence of a variety of natural morphologies, from mountain to alluvial environments, as well as the presence of significant civil and industrial settlements, it makes it a suitable case study to apply the proposed approach. The latter has been applied over three different flood events that occurred from November to December 2014. The results confirm that the use of merged gauge–satellite data using the cellular automata algorithm improves the performance of the hydrological simulation, as also confirmed by the statistical analysis performed for 17 selected quality scores. [ABSTRACT FROM AUTHOR]

An air quality model ensemble is used to represent the current state-of-the-art in atmospheric modeling, composed of two global forecasts and two regional simulations. The model ensemble assessment focuses on both carbonaceous aerosols, i.e. black carbon (BC) and organic aerosol (OA), and five trace gases during two aircraft campaigns of the EMeRGe (Effect of Megacities on the Transport and Transformation of Pollutants on the Regional to Global Scales) project. These campaigns, designed with similar flight plans for Europe and Asia, along with identical instrumentation, provide a unique opportunity to evaluate air quality models with a specific focus on city plumes. The observed concentration ranges for all pollutants are reproduced by the ensemble in the various environments sampled during the EMeRGe campaigns. The evaluation of the air quality model ensemble reveals differences between the two campaigns, with carbon monoxide (CO) better reproduced in East Asia, while other studied pollutants exhibit a better agreement in Europe. These differences may be associated to the modeling of biomass burning pollution during the EMeRGe Asian campaign. However, the modeled CO generally demonstrates good agreement with observations with a correlation coefficient (R) of ≈ 0.8. For formaldehyde (HCHO), nitrogen dioxide (NO2), ozone (O3) and BC the agreement is moderate (with R ranging from 0.5 to 0.7), while for OA and SO2 the agreement is weak (with R ranging from 0.2 to 0.3). The modeled wind speed shows very good agreement (R ≈ 0.9). This supports the use of modeled pollutant transport to identify flight legs associated with pollution originating from major population centers targeted among different flight plans. City plumes are identified using a methodology based on numerical tracer experiments, where tracers are emitted from city centers. This approach robustly localizes the different city plumes in both time and space, even after traveling several hundred kilometers. Focusing on city plumes, the fractions of high concentration are overestimated for BC, OA, HCHO, and SO2, which degrades the performance of the ensemble. This assessment of air quality models with collocated airborne measurements provides a clear insight into the existing limitations in modeling the composition of carbonaceous aerosols and trace gases, especially in city plumes. [ABSTRACT FROM AUTHOR]

The concentration of carbonaceous aerosols, black carbon (BC) and organic aerosol (OA), in the atmosphere is related to co-emitted or co-produced trace gases. In this study, we investigate the most relevant proportional relationships between both BC and OA with the following trace gases: carbon monoxide (CO), formaldehyde (HCHO), nitrogen dioxide (NO2), ozone (O3), and sulfur dioxide (SO2). One motivation for selecting these trace gases is that they can be observed using remote sensing measurements from satellite instrumentation, and could therefore be used to predict spatial changes in the amounts of BC and OA. Airborne measurements are optimal for the analysis of both the composition of aerosols and trace gases in different environments ranging from unpolluted oceanic air masses to those in heavily polluted city plumes. The two aircraft campaigns of the EMeRGe (Effect of Megacities on the Transport and Transformation of Pollutants on the Regional to Global Scales) project have created a unique database, with flight plans dedicated to studying city plumes in two regions, Europe (2017) and East Asia (2018), along with identical instrumental payload. Using linear regression analysis, three relevant relationships between carbonaceous aerosol and trace gases are identified: - The BC/OA ratio observed in the Asian campaign is three times higher (≈ 0.3) than in the European campaign (≈ 0.1), whereas the Pearson correlation coefficient (R) between BC and OA is much higher in Europe (R ≈ 0.8) than in Asia (R ≈ 0.6). - The CO/BC ratio is also observed higher in the Asian campaign (≈ 240) than in the European campaign (≈ 170), whereas the R-value between CO and BC is similar for both campaigns (R ≈ 0.7). - The HCHO/OA ratio is similar in both campaigns (≈0.32), but the observed R-values between HCHO and OA is higher in Europe than in the Asia (R ≈ 0.7 compared to ≈ 0.3). By focusing on heavily polluted air masses sampled downwind in the city plumes, the ratios between the observed carbonaceous aerosols and the five trace gases change, and the R-values increase with O3 for both BC and OA (R ≈ 0.5). To assess the performance of atmospheric models with respect to the most relevant observed relationships, an air quality model ensemble is used to represent the current state of atmospheric modeling, consisting of two global and two regional simulations. The evaluation shows that these proportional relationships are not satisfactorily reproduced by the model ensemble. The relationships between BC and OA or between CO and BC are modeled with stronger correlations than the observed ones, and their higher ratios observed in Asia compared to Europe are not reproduced. Furthermore, the modeled HCHO/OA ratio is underestimated in the Asian campaign and overestimated in the European campaign. This analysis of the proportional relationships between carbonaceous aerosols and trace gases implies that the observed relationships can be used to constrain models and improve anthropogenic emission inventories. In addition, it implies that information about the lower tropospheric concentration of carbonaceous aerosols can potentially be inferred from satellite retrievals of trace gases, particularly in the plumes from megacities. [ABSTRACT FROM AUTHOR]

The uncertainty of hydrological forecasts is strongly related to the uncertainty of the rainfall field due to the nonlinear relationship between the spatio-temporal pattern of rainfall and runoff. Rain gauges are typically considered as reference data to rebuild precipitation fields. However, due to the density and the distribution variability of the raingauge network, the rebuilding of the precipitation field can be affected by severe errors which compromise the hydrological simulation output. On the other hand, retrievals obtained from remote sensing observations provide spatially resolved precipitation fields improving their representativeness. In this regard, this paper aims to investigate the impact of using the merged rainfall fields from the rain gauge Italian rainfall network and the NASA Global Precipitation Measurement (GPM) IMERG precipitation product on the hydrological simulation performance. In particular, one aspect is to highlight the benefits of applying the Cellular Automata algorithm to pre-process input data in order to merge them and reconstruct an improved version of the precipitation field. The cellular automata approach is evaluated in the Tanaro River Basin, one of the tributaries of the Po River in Italy. As this site is characterized by the coexistence of a variety of natural morphologies, from mountain to alluvial environments, as well as the presence of significant civil and industrial settlements, it makes it a suitable case study to apply the proposed approach. The latter has been applied over three different flood events occurred from November to December 2014. The results confirm that the use of merged gauge-satellite data using the Cellular Automata algorithm improves the performance of the hydrological simulation, as also confirmed by the statistical analysis performed for seventeen selected quality scores. [ABSTRACT FROM AUTHOR]

Italy is a territory characterized by complex topography with the Apennines mountain range crossing the entire peninsula and its highest peaks in central Italy. Using the latter as our area of interest and the snow seasons 2018/19, 2019/20 and 2020/21, the goal of this study is to investigate the ability of a simple single-layer and a more sophisticated multi-layer snow cover numerical model to reproduce the observed snow height, snow water equivalent and snow extent in the central Apennines, using for both models the same forecast weather data as meteorological forcing. We here consider two well-known ground surface and soil models: (i) Noah LSM, an Eulerian model which simulates the snowpack as a bulk single layer, and (ii) Alpine3D, a multi-layer Lagrangian model which simulates the snowpack stratification. We adopt the Weather Research and Forecasting (WRF) model to produce the meteorological data to drive both Noah LSM and Alpine3D at a regional scale with a spatial resolution of 3 km. While Noah LSM is already online-coupled with the WRF model, we develop here a dedicated offline coupling between WRF and Alpine3D. We validate the WRF simulations of surface meteorological variables in central Italy using a dense network of automatic weather stations, obtaining correlation coefficients higher than 0.68, except for wind speed, which suffered from the model underestimation of the real elevation. The performances of both WRF–Noah and WRF–Alpine3D are evaluated by comparing simulated and measured snow height, snow height variation and snow water equivalent, provided by a quality-controlled network of automatic and manual snow stations located in the central Apennines. We find that WRF–Alpine3D can predict better than WRF–Noah the snow height and the snow water equivalent, showing a correlation coefficient with the observations of 0.9 for the former and 0.7 for the latter. Both models show similar performances in reproducing the observed daily snow height variation; nevertheless WRF–Noah is slightly better at predicting large positive variations, while WRF–Alpine3D can slightly better simulate large negative variations. Finally we investigate the abilities of the models in simulating the snow cover area fraction, and we show that WRF–Noah and WRF–Alpine3D have almost equal skills, with both models overestimating it. The equal skills are also confirmed by Jaccard and the average symmetric surface distance indices. [ABSTRACT FROM AUTHOR]