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Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
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Volume 17, issue 5 | Copyright
Atmos. Chem. Phys., 17, 3253-3278, 2017
https://doi.org/10.5194/acp-17-3253-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 07 Mar 2017

Research article | 07 Mar 2017

Sensitivity of the interannual variability of mineral aerosol simulations to meteorological forcing dataset

Molly B. Smith1,2, Natalie M. Mahowald1, Samuel Albani1,3, Aaron Perry1, Remi Losno4, Zihan Qu4,5, Beatrice Marticorena5, David A. Ridley6, and Colette L. Heald6 Molly B. Smith et al.
  • 1Department of Earth and Atmospheric Sciences, Cornell University, Ithaca, NY 14850, USA
  • 2Department of Atmospheric and Environmental Sciences, University at Albany, State University of New York, Albany, NY 12222, USA
  • 3Laboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ, Gif-sur-Yvette, France
  • 4Institut de Physique du Globe de Paris, University of Paris Diderot, USPC, CNRS – UMR7154, Paris, France
  • 5LISA, Universites Paris Est-Paris Diderot-Paris 7, CNRS – UMR7583, Creteil, France
  • 6Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Massachusetts, MA 02139, USA

Abstract. Interannual variability in desert dust is widely observed and simulated, yet the sensitivity of these desert dust simulations to a particular meteorological dataset, as well as a particular model construction, is not well known. Here we use version 4 of the Community Atmospheric Model (CAM4) with the Community Earth System Model (CESM) to simulate dust forced by three different reanalysis meteorological datasets for the period 1990–2005. We then contrast the results of these simulations with dust simulated using online winds dynamically generated from sea surface temperatures, as well as with simulations conducted using other modeling frameworks but the same meteorological forcings, in order to determine the sensitivity of climate model output to the specific reanalysis dataset used. For the seven cases considered in our study, the different model configurations are able to simulate the annual mean of the global dust cycle, seasonality and interannual variability approximately equally well (or poorly) at the limited observational sites available. Overall, aerosol dust-source strength has remained fairly constant during the time period from 1990 to 2005, although there is strong seasonal and some interannual variability simulated in the models and seen in the observations over this time period. Model interannual variability comparisons to observations, as well as comparisons between models, suggest that interannual variability in dust is still difficult to simulate accurately, with averaged correlation coefficients of 0.1 to 0.6. Because of the large variability, at least 1 year of observations at most sites are needed to correctly observe the mean, but in some regions, particularly the remote oceans of the Southern Hemisphere, where interannual variability may be larger than in the Northern Hemisphere, 2–3 years of data are likely to be needed.

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Using different meteorology reanalyses to drive dust in climate modeling can produce dissimilar global dust distributions, especially in the Southern Hemisphere (SH). It may therefore not be advisable for SH dust studies to base results on simulations driven by one reanalysis. Northern Hemisphere dust varies mostly on seasonal timescales, while SH dust varies on interannual timescales. Dust is an important part of climate modeling, and we hope this contributes to understanding these simulations.
Using different meteorology reanalyses to drive dust in climate modeling can produce dissimilar...
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