2016
DOI: 10.5194/gmd-9-607-2016
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Quantifying the impact of sub-grid surface wind variability on sea salt and dust emissions in CAM5

Abstract: Abstract. This paper evaluates the impact of sub-grid variability of surface wind on sea salt and dust emissions in the Community Atmosphere Model version 5 (CAM5). The basic strategy is to calculate emission fluxes multiple times, using different wind speed samples of a Weibull probability distribution derived from model-predicted grid-box mean quantities.In order to derive the Weibull distribution, the sub-grid standard deviation of surface wind speed is estimated by taking into account four mechanisms: turb… Show more

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Cited by 26 publications
(35 citation statements)
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“…Gustafson et al [] further showed that the neglect of subgrid processes over Mexico could lead to an average daytime mean bias of over 30% in shortwave aerosol radiative forcing obtained from WRF‐Chem simulations. In addition, Zhang et al [] investigated the effect of subgrid wind variability on sea‐salt and dust emission in the community atmospheric model (CAM5). They found that the simulated global dust emissions could increase by 50% when considering the subgrid wind variability, while the subgrid wind variability has relatively small impacts (about 7% increase) on global averaged sea‐salt emissions.…”
Section: Introductionsupporting
confidence: 91%
“…Gustafson et al [] further showed that the neglect of subgrid processes over Mexico could lead to an average daytime mean bias of over 30% in shortwave aerosol radiative forcing obtained from WRF‐Chem simulations. In addition, Zhang et al [] investigated the effect of subgrid wind variability on sea‐salt and dust emission in the community atmospheric model (CAM5). They found that the simulated global dust emissions could increase by 50% when considering the subgrid wind variability, while the subgrid wind variability has relatively small impacts (about 7% increase) on global averaged sea‐salt emissions.…”
Section: Introductionsupporting
confidence: 91%
“…To better understand the model biases in East Asia, dust emission schemes of Kok, Mahowald, et al (), Kok, Albani, et al () (hereafter K14) and Ginoux et al () (hereafter G01) were implemented in the Community Land Model version 4 (CLM4.0) of CESM. Treatment of subgrid surface wind variability in calculating dust emission by Zhang et al () was also applied in CLM4.0. The goal of this study is to evaluate the performance of CESM over East Asia in the simulations of (1) dust extinction profiles and DOD, including their spatial distributions and temporal variations; (2) dust mass budgets in the Taklamakan desert and Tibetan Plateau (see Figure ); and (3) dust surface concentrations and 10‐m wind speed with a special focus on their PDFs.…”
Section: Introductionmentioning
confidence: 99%
“…Moreover, a recent study describing a conceptual model of dust dynamics showed that vertical transport can efficiently counteracts and limits the gravitational settling of coarse particles [103], according to Lidar observations in the frame of the SALTRACE experiment. The evolution of the particle sizes is also shown in Figure 7 [35][36][37][38][39][40] • N resulting mainly of the trans-Pacific transport of Asian dust emissions mixed with diluted Saharan dust transported by southwesterly fluxes. In Figure 7, the deep convection updraft flux integrated over the column is displayed as an indicator of the convective activity.…”
Section: Particle Size Distributionmentioning
confidence: 90%
“…A major West African dust storm was simulated at 5 km resolution with the French modeling system AROME coupled with the ORILAM aerosol model [37]. A high-resolution modeling of dust phenomena is computationally demanding and requires high-resolution input fields [38], however, high resolution simulations ensure a better quantification of dust source regions, meteorological mechanisms that control dust emission fluxes [39,40], transport pathways [41,42], dust radiative direct and indirect effects, complex atmospheric chemistry, and deposition processes. For anthropogenic pollution, a statistical evaluation performed by [43] showed that the increased resolution better reproduces the spatial gradients in pollution regimes, but does not help to improve significantly the model performance for reproducing observed temporal variability.…”
Section: Introductionmentioning
confidence: 99%