Cloud ice caused by atmospheric mineral dust – Part 1: Parameterization of ice nuclei concentration in the NMME-DREAM model

Abstract

Abstract. Dust aerosols are very efficient ice nuclei, important for heterogeneous cloud glaciation even in regions distant from desert sources. A new generation of ice nucleation parameterizations, including dust as an ice nucleation agent, opens the way towards a more accurate treatment of cold cloud formation in atmospheric models. Using such parameterizations, we have developed a regional dust-atmospheric modelling system capable of predicting, in real time, dustinduced ice nucleation. We executed the model with the added ice nucleation component over the Mediterranean region, exposed to moderate Saharan dust transport, over two periods lasting 15 and 9 days, respectively. The model results were compared against satellite and ground-based cloud-icerelated measurements, provided by SEVIRI (Spinning Enhanced Visible and InfraRed Imager) and the CNR-IMAA Atmospheric Observatory (CIAO) in Potenza, southern Italy. The predicted ice nuclei concentration showed a reasonable level of agreement when compared against the observed spatial and temporal patterns of cloud ice water. The developed methodology permits the use of ice nuclei as input into the cloud microphysics schemes of atmospheric models, assuming that this approach could improve the predictions of cloud formation and associated precipitation.

Development of a dynamic dust source map for NMME-DREAM v1.0 model based on MODIS Normalized Difference Vegetation Index (NDVI) over the Arabian Peninsula

“…The meteorological core is the NCEP-NMME atmospheric model (Janjic et al, 2001). DREAM v1.0 is a numerical model created with the main purpose of simulating and predicting the atmospheric life cycle of mineral dust using an Euler-type nonlinear partial differential equation for dust mass continuity (Nickovic et al, , 2016Pérez et al, 2006;Pejanovic et al, 2011). In DREAM the concentration approach is used for dust uplift, where surface concentration is used as a lower boundary condition and used for the calculation of surface fluxes, which in turn depends on the friction velocity .…”

“…Level 2 datasets were used for all stations apart from Kuwait University, where only Level 1.5 data were available. Both model and AERONET AODs were calculated at 532 nm; this was chosen to facilitate future intercomparing with lidar systems that frequently measure at this wavelength (e.g., Pappalardo et al, 2014). AERONET measurements were converted to this wavelength using the 440-870 Ångström exponent and taking into account AOD measurements at 440, 675, and 870 nm; in the cases where the 440 nm AOD was not available, the 500 nm (Mezaira) or 443 nm (KAUST campus) measurement was used instead.…”

“…Dust is a climatic regulator, as it modifies extensively the radiative balance of the atmospheric column (e.g., Torge et al, 2011;Spyrou et al, 2013;Mahowald et al, 2014). At the same time dust aerosols modify the atmospheric water content (Spyrou, 2018), the way clouds are formed by acting as cloud condensation nuclei (CCN) and ice nuclei (IN), and the precipitation process (Kumar et al, 2011;Solomos et al, 2011;Nickovic et al, 2016). In addition, there is a clear connection between dust particles and human health disorders, as the size of the aerosols produced is small enough to cause respiratory and cardiovascular diseases, as well as pathogenic conditions due to the microorganisms that they can potentially carry (Mitsakou et al, 2008;Esmaeil et al, 2014).…”

“…Usually the definition of the areas that can act as dust sources is made using global datasets. For example Nickovic et al (2001) used a subjective correspondence between the Olson World Ecosystems (Olson et al, 1983) and the 13 SSib (simplified simple biosphere; Xue et al, 1991) vegetation types to identify arid and semiarid areas. Similarly, Spyrou et al (2010) used a 30 s global land use/cover database, classified according to the 24 category U.S. Geological Survey (USGS) land use/cover system (Anderson et al, 1976) to define active areas in the SKIRON dust model.…”

Potential of polarization/Raman lidar to separate fine dust, coarse dust, maritime, and anthropogenic aerosol profiles

“…The retrieval of these higher level lidar products is driven by the fact that state-of-the-art environmental (regional) and global atmospheric models include aerosol schemes which simulate microphysical aerosol processes and life cycles of a variety of aerosol components and can be used to predict dust events and enable the simulation of CCN and INP profiles (Huneeus et al, 2011;Koffi et al, 2012Koffi et al, , 2016Mann et al, 2014;Kim et. al., 2014;Nickovic et al, 2016;Hande et al, 2016). These models allow detailed dust climate impact studies and forecasts of fine and coarse dust fractions and thus of the fine dust contribution to the measured overall fine particle concentration (PM 1 , particles with diameters < 1 µm), which is of great interest for environmental services (air-quality management), especially in areas close to deserts.…”