Desert dust interacts with virtually every component of the Earth system, including the climate system. We develop a new methodology to represent the global dust cycle that integrates observational constraints on the properties and abundance of desert dust with global atmospheric model simulations. We show that the resulting representation of the global dust cycle is more accurate than can be obtained from a large number of current climate global atmospheric models.
Desert dust interacts with virtually every component of the Earth system, including the climate...
Review status: this preprint is currently under review for the journal ACP.
Improved representation of the global dust cycle using
observational constraints on dust properties and abundance
Jasper F. Kok1,Adeyemi A. Adebiyi1,Samuel Albani2,3,Yves Balkanski3,Ramiro Checa-Garcia3,Mian Chin4,Peter R. Colarco4,Douglas Stephen Hamilton5,Yue Huang1,Akinori Ito6,Martina Klose7,Danny M. Leung1,Longlei Li5,Natalie M. Mahowald5,Ron L. Miller8,Vincenzo Obiso7,8,Carlos Pérez García-Pando7,9,Adriana Rocha-Lima10,11,Jessica S. Wan5,and Chloe A. Whicker1Jasper F. Kok et al.Jasper F. Kok1,Adeyemi A. Adebiyi1,Samuel Albani2,3,Yves Balkanski3,Ramiro Checa-Garcia3,Mian Chin4,Peter R. Colarco4,Douglas Stephen Hamilton5,Yue Huang1,Akinori Ito6,Martina Klose7,Danny M. Leung1,Longlei Li5,Natalie M. Mahowald5,Ron L. Miller8,Vincenzo Obiso7,8,Carlos Pérez García-Pando7,9,Adriana Rocha-Lima10,11,Jessica S. Wan5,and Chloe A. Whicker1
Received: 29 Oct 2020 – Accepted for review: 19 Nov 2020 – Discussion started: 23 Nov 2020
Abstract. Even though desert dust is the most abundant aerosol by mass in Earth's atmosphere, atmospheric models struggle to accurately represent its spatial and temporal distribution. These model errors are partially caused by fundamental difficulties in simulating dust emission in coarse-resolution models and in accurately representing dust microphysical properties. Here we mitigate these problems by developing a new methodology that yields an improved representation of the global dust cycle. We present an analytical framework that uses inverse modeling to integrate an ensemble of global model simulations with observational constraints on the dust size distribution, extinction efficiency, and regional dust aerosol optical depth. We then compare the inverse model results against independent measurements of dust surface concentration and deposition flux and find that errors are reduced by approximately a factor of two relative to current model simulations of the Northern Hemisphere dust cycle. The inverse model results show smaller improvements in the less dusty Southern Hemisphere, most likely because both the model simulations and the observational constraints used in the inverse model are less accurate. On a global basis, we find that the emission flux of dust with geometric diameter up to 20 μm (PM20) is approximately 5,000 Tg/year, which is greater than most models account for. This larger PM20 dust flux is needed to match observational constraints showing a large atmospheric loading of coarse dust. We obtain gridded data sets of dust emission, vertically integrated loading, dust aerosol optical depth, (surface) concentration, and wet and dry deposition fluxes that are resolved by season and particle size. As our results indicate that this data set is more accurate than current model simulations and the MERRA-2 dust reanalysis product, it can be used to improve quantifications of dust impacts on the Earth system.
Desert dust interacts with virtually every component of the Earth system, including the climate system. We develop a new methodology to represent the global dust cycle that integrates observational constraints on the properties and abundance of desert dust with global atmospheric model simulations. We show that the resulting representation of the global dust cycle is more accurate than can be obtained from a large number of current climate global atmospheric models.
Desert dust interacts with virtually every component of the Earth system, including the climate...