Journal article

Atmospheric dust modeling from meso to global scales with the online NMMB/BSC-Dust model – Part 1: Model description, annual simulations and evaluation

Pérez C, Haustein K, Janjic Z, Jorba O, Huneeus N, Baldasano J, Black T, Basart S, Nickovic S, Miller R, Perlwitz J, Schulz M, Thomson M ...see all

Atmospheric Chemistry and Physics, vol. 11, issue 24 (2011) pp. 13001-13027

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Abstract

We describe and evaluate the NMMB/BSC-Dust, a new dust aerosol cycle model embedded online within the NCEP Non-hydrostatic Multiscale Model (NMMB). NMMB is a further evolution of the operational Non-hydrostatic Mesoscale Model (WRF-NMM), which together with other upgrades has been extended from meso to global scales. Its unified non-hydrostatic dynamical core is pre-pared for regional and global simulation domains. The new NMMB/BSC-Dust is intended to provide short to medium-range weather and dust forecasts from regional to global scales and represents a first step towards the development of a unified chemical-weather model. This paper describes the parameterizations used in the model to simulate the dust cycle including sources, transport, deposition and interaction with radiation. We evaluate monthly and annual means of the global configuration of the model against the AEROCOM dust benchmark dataset for year 2000 including surface con-centration, deposition and aerosol optical depth (AOD), and we evaluate the daily AOD variability in a regional domain at high resolution covering Northern Africa, Middle East and Europe against AERONET AOD for year 2006. The NMMB/BSC-Dust provides a good description of the hori-Correspondence to: C. Pérez (carlos.perezga@nasa.gov) zontal distribution and temporal variability of the dust. Daily AOD correlations at the regional scale are around 0.6–0.7 on average without dust data assimilation. At the global scale the model lies within the top range of AEROCOM dust mod-els in terms of performance statistics for surface concentra-tion, deposition and AOD. This paper discusses the current strengths and limitations of the modeling system and points towards future improvements.

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