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Title: Predicting the mineral composition of dust aerosols – Part 2: Model evaluation and identification of key processes with observations

Abstract

A global compilation of nearly sixty measurement studies is used to evaluate two methods of simulating the mineral composition of dust aerosols in an Earth system model. Both methods are based upon a Mean Mineralogical Table (MMT) that relates the soil mineral fractions to a global atlas of arid soil type. The Soil Mineral Fraction (SMF) method assumes that the aerosol mineral fractions match the fractions of the soil. The MMT is based upon soil measurements after wet sieving, a process that destroys aggregates of soil particles that would have been emitted from the original, undisturbed soil. The second method approximately reconstructs the emitted aggregates. This model is referred to as the Aerosol Mineral Fraction (AMF) method because the mineral fractions of the aerosols differ from those of the wet-sieved parent soil, partly due to reaggregation. The AMF method remedies some of the deficiencies of the SMF method in comparison to observations. Only the AMF method exhibits phyllosilicate mass at silt sizes, where they are abundant according to observations. In addition, the AMF quartz fraction of silt particles is in better agreement with measured values, in contrast to the overestimated SMF fraction. Measurements at distinct clay and silt particle sizesmore » are shown to be more useful for evaluation of the models, in contrast to the sum over all particles sizes that is susceptible to compensating errors, as illustrated by the SMF experiment. Model errors suggest that allocation of the emitted silt fraction of each mineral into the corresponding transported size categories is an important remaining source of uncertainty. Evaluation of both models and the MMT is hindered by the limited number of size-resolved measurements of mineral content that sparsely sample aerosols from the major dust sources. In conclusion, the importance of climate processes dependent upon aerosol mineral composition shows the need for global and routine mineral measurements.« less

Authors:
ORCiD logo [1];  [1];  [1]
  1. Columbia Univ., New York, NY (United States). Dept. of Applied Physics and Applied Mathematics; NASA Goddard Inst. for Space Studies (GISS), New York, NY (United States)
Publication Date:
Research Org.:
Columbia Univ., New York, NY (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1441393
Grant/Contract Number:  
SC0006713
Resource Type:
Accepted Manuscript
Journal Name:
Atmospheric Chemistry and Physics (Online)
Additional Journal Information:
Journal Name: Atmospheric Chemistry and Physics (Online); Journal Volume: 15; Journal Issue: 20; Journal ID: ISSN 1680-7324
Publisher:
European Geosciences Union
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY

Citation Formats

Perlwitz, J. P., Perez Garcia-Pando, C., and Miller, R. L. Predicting the mineral composition of dust aerosols – Part 2: Model evaluation and identification of key processes with observations. United States: N. p., 2015. Web. doi:10.5194/acp-15-11629-2015.
Perlwitz, J. P., Perez Garcia-Pando, C., & Miller, R. L. Predicting the mineral composition of dust aerosols – Part 2: Model evaluation and identification of key processes with observations. United States. doi:10.5194/acp-15-11629-2015.
Perlwitz, J. P., Perez Garcia-Pando, C., and Miller, R. L. Wed . "Predicting the mineral composition of dust aerosols – Part 2: Model evaluation and identification of key processes with observations". United States. doi:10.5194/acp-15-11629-2015. https://www.osti.gov/servlets/purl/1441393.
@article{osti_1441393,
title = {Predicting the mineral composition of dust aerosols – Part 2: Model evaluation and identification of key processes with observations},
author = {Perlwitz, J. P. and Perez Garcia-Pando, C. and Miller, R. L.},
abstractNote = {A global compilation of nearly sixty measurement studies is used to evaluate two methods of simulating the mineral composition of dust aerosols in an Earth system model. Both methods are based upon a Mean Mineralogical Table (MMT) that relates the soil mineral fractions to a global atlas of arid soil type. The Soil Mineral Fraction (SMF) method assumes that the aerosol mineral fractions match the fractions of the soil. The MMT is based upon soil measurements after wet sieving, a process that destroys aggregates of soil particles that would have been emitted from the original, undisturbed soil. The second method approximately reconstructs the emitted aggregates. This model is referred to as the Aerosol Mineral Fraction (AMF) method because the mineral fractions of the aerosols differ from those of the wet-sieved parent soil, partly due to reaggregation. The AMF method remedies some of the deficiencies of the SMF method in comparison to observations. Only the AMF method exhibits phyllosilicate mass at silt sizes, where they are abundant according to observations. In addition, the AMF quartz fraction of silt particles is in better agreement with measured values, in contrast to the overestimated SMF fraction. Measurements at distinct clay and silt particle sizes are shown to be more useful for evaluation of the models, in contrast to the sum over all particles sizes that is susceptible to compensating errors, as illustrated by the SMF experiment. Model errors suggest that allocation of the emitted silt fraction of each mineral into the corresponding transported size categories is an important remaining source of uncertainty. Evaluation of both models and the MMT is hindered by the limited number of size-resolved measurements of mineral content that sparsely sample aerosols from the major dust sources. In conclusion, the importance of climate processes dependent upon aerosol mineral composition shows the need for global and routine mineral measurements.},
doi = {10.5194/acp-15-11629-2015},
journal = {Atmospheric Chemistry and Physics (Online)},
number = 20,
volume = 15,
place = {United States},
year = {2015},
month = {10}
}

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