Search for: All records

Abstract The source of dust in the global atmosphere is an important factor to better understand the role of dust aerosols in the climate system. However, it is a difficult task to attribute the airborne dust over the remote land and ocean regions to their origins since dust from various sources are mixed during long‐range transport. Recently, a multi‐model experiment, namely the AeroCom‐III Dust Source Attribution (DUSA), has been conducted to estimate the relative contribution of dust in various locations from different sources with tagged simulations from seven participating global models. The BASE run and a series of runs with nine tagged regions were made to estimate the contribution of dust emitted in East‐ and West‐Africa, Middle East, Central‐ and East‐Asia, North America, the Southern Hemisphere, and the prominent dust hot spots of the Bodélé and Taklimakan Deserts. The models generally agree in large scale mean dust distributions, however models show large diversity in dust source attribution. The inter‐model differences are significant with the global model dust diversity in 30%–50%, but the differences in regional and seasonal scales are even larger. The multi‐model analysis estimates that North Africa contributes 60% of global atmospheric dust loading, followed by Middle East and Central Asia sources (24%). Southern hemispheric sources account for 10% of global dust loading, however it contributes more than 70% of dust over the Southern Hemisphere. The study provides quantitative estimates of the impact of dust emitted from different source regions on the globe and various receptor regions including remote land, ocean, and the polar regions synthesized from the seven models. 

Abstract. Desert dust is an important atmospheric aerosol that affects the Earth's climate, biogeochemistry, and air quality. However, current Earth system models (ESMs) struggle to accurately capture the impact of dust on the Earth's climate and ecosystems, in part because these models lack several essential aeolian processes that couple dust with climate and land surface processes. In this study, we address this issue by implementing several new parameterizations of aeolian processes detailed in our companion paper in the Community Earth System Model version 2 (CESM2). These processes include (1) incorporating a simplified soil particle size representation to calculate the dust emission threshold friction velocity, (2) accounting for the drag partition effect of rocks and vegetation in reducing wind stress on erodible soils, (3) accounting for the intermittency of dust emissions due to unresolved turbulent wind fluctuations, and (4) correcting the spatial variability of simulated dust emissions from native to higher spatial resolutions on spatiotemporal dust variability. Our results show that the modified dust emission scheme significantly reduces the model bias against observations compared with the default scheme and improves the correlation against observations of multiple key dust variables such as dust aerosol optical depth (DAOD), surface particulate matter (PM) concentration, and deposition flux. Our scheme's dust also correlates strongly with various meteorological and land surface variables, implying higher sensitivity of dust to future climate change than other schemes' dust. These findings highlight the importance of including additional aeolian processes for improving the performance of ESM aerosol simulations and potentially enhancing model assessments of how dust impacts climate and ecosystem changes. 

Abstract. Estimating past aerosol radiative effects and their uncertainties is an important topic in climate science. Aerosol radiative effects propagate into large uncertainties in estimates of how present and future climate evolves with changing greenhouse gas emissions. A deeper understanding of how aerosols affected the atmospheric energy budget under past climates is hindered in part by a lack of relevant paleo-observations and in part because less attention has been paid to the problem. Because of the lack of information we do not seek here to determine the change in the radiative forcing due to aerosol changes but rather to estimate the uncertainties in those changes. Here we argue that current uncertainties from emission uncertainties (90 % confidence interval range spanning 2.8 W m−2) are just as large as model spread uncertainties (2.8 W m−2) in calculating preindustrial to present-day aerosol radiative effects. There are no estimates of radiative forcing for important aerosols such as wildfire and dust aerosols in most paleoclimate time periods. However, qualitative analysis of paleoclimate proxies suggests that changes in aerosols between different past climates are similar in magnitude to changes in aerosols between the preindustrial and present day; plus, there is the added uncertainty from the variability in aerosols and fires in the preindustrial. From the limited literature we crudely estimate a paleoclimate aerosol uncertainty for the Last Glacial Maximum relative to preindustrial of 4.8 W m−2, and we estimate the uncertainty in the aerosol feedback in the natural Earth system over the paleoclimate (Last Glacial Maximum to preindustrial) to be about 3.2 W m−2 K−1. In order to more accurately assess the uncertainty in historical aerosol radiative effects, we propose a new model intercomparison project, which would include multiple plausible emission scenarios tested across a range of state-of-the-art climate models over the historical period. These emission scenarios would then be compared to the available independent aerosol observations to constrain which are most probable. In addition, future efforts should work to characterize and constrain paleo-aerosol forcings and uncertainties. Careful propagation of aerosol uncertainties in the literature is required to ensure an accurate quantification of uncertainties in projections of future climate changes. 

Abstract Desert dust accounts for a large fraction of shortwave radiation absorbed by aerosols, which adds to the climate warming produced by greenhouse gases. However, it remains uncertain exactly how much shortwave radiation dust absorbs. Here, we leverage in-situ measurements of dust single-scattering albedo to constrain absorption at mid-visible wavelength by North African dust, which accounts for approximately half of the global dust. We find that climate and chemical transport models overestimate North African dust absorption aerosol optical depth (AAOD) by up to a factor of two. This occurs primarily because models overestimate the dust imaginary refractive index, the effect of which is partially masked by an underestimation of large dust particles. Similar factors might contribute to an overestimation of AAOD retrieved by the Aerosol Robotic Network, which is commonly used to evaluate climate and chemical transport models. The overestimation of dust absorption by models could lead to substantial biases in simulated dust impacts on the Earth system, including warm biases in dust radiative effects. 

Abstract. Most global aerosol models approximate dust as sphericalparticles, whereas most remote sensing retrieval algorithms approximate dust as spheroidal particles with a shape distribution that conflicts withmeasurements. These inconsistent and inaccurate shape assumptions generatebiases in dust single-scattering properties. Here, we obtain dustsingle-scattering properties by approximating dust as triaxial ellipsoidalparticles with observationally constrained shape distributions. We findthat, relative to the ellipsoidal dust optics obtained here, the sphericaldust optics used in most aerosol models underestimate dust single-scattering albedo, mass extinction efficiency, and asymmetry parameter for almost all dust sizes in both the shortwave and longwave spectra. We further find that the ellipsoidal dust optics are in substantially better agreement with observations of the scattering matrix and linear depolarization ratio than the spheroidal dust optics used in most retrieval algorithms. However, relative to observations, the ellipsoidal dust optics overestimate the lidar ratio by underestimating the backscattering intensity by a factor of ∼2. This occurs largely because the computational method used to simulate ellipsoidal dust optics (i.e., the improved geometric optics method) underestimates the backscattering intensity by a factor of ∼2 relative to other computational methods (e.g., the physical geometric optics method). We conclude that the ellipsoidal dust optics with observationally constrained shape distributions can help improve global aerosol models and possibly remote sensing retrieval algorithms that do not use the backscattering signal. 

Abstract. Desert dust accounts for most of the atmosphere's aerosol burden by mass andproduces numerous important impacts on the Earth system. However, currentglobal climate models (GCMs) and land-surface models (LSMs) struggle toaccurately represent key dust emission processes, in part because ofinadequate representations of soil particle sizes that affect the dustemission threshold, surface roughness elements that absorb wind momentum,and boundary-layer characteristics that control wind fluctuations.Furthermore, because dust emission is driven by small-scale (∼ 1 km or smaller) processes, simulating the global cycle of desert dust inGCMs with coarse horizontal resolutions (∼ 100 km) presents afundamental challenge. This representation problem is exacerbated by dustemission fluxes scaling nonlinearly with wind speed above a threshold windspeed that is sensitive to land-surface characteristics. Here, we addressthese fundamental problems underlying the simulation of dust emissions inGCMs and LSMs by developing improved descriptions of (1) the effect of soiltexture on the dust emission threshold, (2) the effects of nonerodibleroughness elements (both rocks and green vegetation) on the surface windstress, and (3) the effects of boundary-layer turbulence on drivingintermittent dust emissions. We then use the resulting revised dust emissionparameterization to simulate global dust emissions in a standalone modelforced by reanalysis meteorology and land-surface fields. We further propose(4) a simple methodology to rescale lower-resolution dust emissionsimulations to match the spatial variability of higher-resolution emissionsimulations in GCMs. The resulting dust emission simulation showssubstantially improved agreement against regional dust emissionsobservationally constrained by inverse modeling. We thus find that ourrevised dust emission parameterization can substantially improve dustemission simulations in GCMs and LSMs.