Sensitivity study of meteorological parameters on mineral aerosol mobilization, transport, and distribution

Abstract

[1] Predicting mineral aerosol distributions is a difficult task due to the episodic nature of the sources and transport. Here we show comparisons between a 22-year simulation of mineral aerosols and satellite and in situ observations. Our results suggest that the model does a good job of predicting atmospheric mineral aerosol distributions, with some discrepancies. In addition, there are differences between our model results and previously published results [e.g., Ginoux et al., 2001]. We conduct several tests of the sensitivity of mineral aerosol simulations to the meteorological data sets and mobilization parameterizations in order to understand the differences. Comparisons between model simulations using National Center for Atmospheric Research/National Center for Environmental Prediction (NCEP/NCAR) and National Aeronautics and Space Administration Data Assimilation Office (NASA DAO) reanalysis data sets show that the model results with the two data sets are fairly consistent but with some important differences. The sensitivity analysis shows that differences between simulated dust near Australia are likely due to differences in both source parameterization and surface winds. Differences over East Asia are dominated by differences in meteorology. The sensitivity analysis also shows that we cannot tell from comparisons with observations whether the cultivation source is active nor eliminate it because of the large uncertainty in meteorology and source parameterization.

West African monsoon decadal variability and surface-related forcings: second West African Monsoon Modeling and Evaluation Project Experiment (WAMME II)

“…Aerosols have been proposed as a factor contributing to late twentieth century drought in the Sahel via direct and indirect effects (e.g., Yoshioka et al 2007;Kim et al 2010;Ackerley et al 2011;Booth et al 2012;Zhang et al 2013). The WAMME II has applied two aerosol data for the WAMME II aerosol experiment: GOCART data (Chin et al 2014) and the data from the Model of Atmospheric Transport and Chemistry Luo et al 2003). In view of the reduced number of ensemble members in the aerosol experiment, however, we have only compared the SST and LULCC effects.…”

Global trends in visibility: implications for dust sources

“…Sources for different aerosols follow (Rasch et al, 2001) and the simulations are for 1995-2000. The mineral aerosol source model in these simulations is based on the Dust Entrainment and Deposition model (Zender et al, 2003a), as implemented and evaluated by (Mahowald et al, 2002(Mahowald et al, , 2003bLuo et al, 2003). The model uses a friction wind velocity cubed relationship to determine the sources, and a preferential source defined by Ginoux et al (2001), which assumes that natural topographic lows (representing dry lake beds) are dust sources.…”

“…Attempts to determine the impacts N. M. Mahowald et al: Global trends in visibility: implications for dust sources of global scale anthropogenic land use on dust emissions are hampered by the similarity in the spatial distribution of land use derived dust and natural dust for some sources (e.g. Mahowald et al, 2002Mahowald et al, , 2004Luo et al, 2003). Results of modeling simulations suggest that humans have either increased or decreased dust since preindustrial times, depending on the relative importance of human land use, carbon dioxide fertilization and climate change in driving dust .…”

Quantifying mineral dust mass budgets:Terminology, constraints, and current estimates

“…This range would increase if researchers pub lished the uncertainty that arises from input field uncertainty and discretization. Luo et al [2003] showed that different analyzed mete orology data sets (NCEP and DAO) produce, in the same transport model, total dust estimates which differ by more than the total estimated anthropogenic dust mass.…”