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  • 1
    Publication Date: 2024-03-05
    Description: 〈title xmlns:mml="http://www.w3.org/1998/Math/MathML"〉Abstract〈/title〉〈p xmlns:mml="http://www.w3.org/1998/Math/MathML" xml:lang="en"〉Light‐absorbing impurities such as mineral dust can play a major role in reducing the albedo of snow surfaces. Particularly in spring, deposited dust particles lead to increased snow melt and trigger further feedbacks at the land surface and in the atmosphere. Quantifying the extent of dust‐induced variations is difficult due to high variability in the spatial distribution of mineral dust and snow. We present an extension of a fully coupled atmospheric and land surface model system to address the impact of mineral dust on the snow albedo across Eurasia. We evaluated the short‐term effects of Saharan dust in a case study. To obtain robust results, we performed an ensemble simulation followed by statistical analysis. Mountainous regions showed a strong impact of dust deposition on snow depth. We found a mean significant reduction of −1.4 cm in the Caucasus Mountains after 1 week. However, areas with flat terrain near the snow line also showed strong effects despite lower dust concentrations. Here, the feedback to dust deposition was more pronounced as increase in surface temperature and air temperature. In the region surrounding the snow line, we found an average significant surface warming of 0.9 K after 1 week. This study shows that the impact of mineral dust deposition depends on several factors. Primarily, these are altitude, slope, snow depth, and snow cover fraction. Especially in complex terrain, it is therefore necessary to use fully coupled models to investigate the effects of mineral dust on snow pack and the atmosphere.〈/p〉
    Description: Plain Language Summary: Dust particles such as Saharan dust can darken snow surfaces, leading to increased absorption of solar radiation. The result is earlier snow melt in the spring and a warming of the land surface. Predicting dust deposition and subsequent regional impacts is difficult because the distribution of snow and dust appears in complex patterns depending on the landscape. We extended an atmospheric and land surface model system to investigate the impact of Saharan dust particles across Eurasia during a Saharan dust transport event. We found that mountainous regions are particularly affected by the dust particles, leading to increased snowmelt. In addition, regions with thin and patchy snow cover show a strong response to the dust particles, mainly causing a warming of the land surface. We found that the effects of dust particles depend on different regional characteristics. Therefore, when investigating dust on snow, it is important to use model systems that represent both the atmospheric process and surface properties properly.〈/p〉
    Description: Key Points: 〈list list-type="bullet"〉 〈list-item〉 〈p xml:lang="en"〉There are regional effects due to the high spatial variability in mineral dust and snow properties〈/p〉〈/list-item〉 〈list-item〉 〈p xml:lang="en"〉Thin snow layers favor a rise in temperature, higher elevations mainly show accelerated snow melt〈/p〉〈/list-item〉 〈list-item〉 〈p xml:lang="en"〉We found a significant impact on surface radiation, temperature and snow cover properties〈/p〉〈/list-item〉 〈/list〉 〈/p〉
    Description: Initiative and Networking Fund of the Helmholtz Association
    Description: https://doi.org/10.35097/1579
    Keywords: ddc:551.5 ; light‐absorbing impurities ; dust on snow ; snow albedo ; regional impact ; modeling ; ensemble simulation
    Language: English
    Type: doc-type:article
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  • 2
    Publication Date: 2022-03-25
    Description: Reliable and accurate weather forecasts, particularly those of rainfall and its extremes, have the potential to improve living conditions in densely populated southern West Africa (SWA). The limited availability of observations has long impeded a rigorous evaluation of current state-of-the-art forecast models. The field campaign of the Dynamics-Aerosol-Chemistry-Cloud Interactions in West Africa (DACCIWA) project in June–July 2016 has created an unprecedentedly dense set of measurements from surface stations and radiosondes. Here we present results from a comprehensive evaluation of both numerical model forecasts and satellite products using these data on a regional and local level. Results reveal a substantial observational uncertainty showing considerable underestimations in satellite estimates of rainfall and low-cloud cover with little correlation at the local scale. Models have a dry bias of 0.1–1.9 mm·day−1 in rainfall and too low column relative humidity. They tend to underestimate low clouds, leading to excess surface solar radiation of 43 W·m−2. Remarkably, most models show some skill in representing regional modulations of rainfall related to synoptic-scale disturbances, while local variations in rainfall and cloudiness are hardly captured. Slightly better results are found with respect to temperature and for the post-onset rather than for the pre-onset period. Delicate local features such as the Maritime Inflow phenomenon are also rather poorly represented, leading to too cool, dry and cloudy conditions at the coast. Differences between forecast days 1 and 2 are relatively small and hardly systematic, suggesting a relatively quick error saturation. Using explicit convection leads to more realistic spatial variability in rainfall, but otherwise no marked improvement. Future work should aim at improving the subtle balance between the diurnal cycles of low clouds, surface radiation, the boundary layer and convection. Further efforts are also needed to improve the observational system beyond field campaign periods.
    Keywords: ddc:551.6
    Language: English
    Type: doc-type:article
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  • 3
    Publication Date: 2021-10-27
    Description: We show that there is a strong sensitivity of cloud microphysics to model time step in idealized convection-permitting simulations using the COnsortium for Small-scale MOdeling model. Specifically, we found a 53% reduction in precipitation when the time step is increased from 1 to 15 s, changes to the location of precipitation and hail reaching the surface, and changes to the vertical distribution of hydrometeors. The effect of cloud condensation nuclei perturbations on precipitation also changes both magnitude and sign with the changing model time step. The sensitivity arises because of the numerical implementation of processes in the model, specifically the so-called “splitting” of the dynamics (e.g., advection and diffusion) and the parameterized physics (e.g., microphysics scheme). Calculating one step at a time (sequential-update splitting) gives a significant time step dependence because large supersaturation with respect to liquid is generated in updraft regions, which strongly affect parameterized microphysical process rates—in particular, ice nucleation. In comparison, calculating both dynamics and microphysics using the same inputs of temperature and water vapor (hybrid parallel splitting) or adding an additional saturation adjustment within the dynamics reduces the time step sensitivity of surface precipitation by limiting the supersaturation seen by the microphysics, although sensitivity to time step remains for some processes.
    Keywords: 551.5 ; convection permitting ; microphysics ; time step ; parallel splitting ; saturation adjustment ; physics-dynamics coupling
    Language: English
    Type: map
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