Quantifying mineral dust mass budgets:Terminology, constraints, and current estimates

Eos, Vol. 85, No. 48, 30 November 2004 Quantifying Mineral Dust Mass Budgets:Terminology, Constraints, and Current Estimates PAGES 5 0 9 , 5 1 2 Current Estimates and Constraints Mineral dust aerosol is created by wind erosion of soil particles. In addition to its direct radiative effect, dust aerosol mediates o c e a n carbon uptake and the chemical cycles of other aerosols like sulfates. Dust observations during the past d e c a d e span measurements of local concen­ tration and deposition to global satellite retrievals of aerosol optical thickness [Prospeto et al, 2002]. The size range of particles represented by each model is crucial for comparing emission estimates. If counted, short-lived large particles dominate mass emission budgets. For intercomparison purposes, models and measurements should provide emission (and deposition) estimates in a standard size range. Diameter D < 10 um is recommended.This includes a majority of the far-traveled particles that are distributed glob­ ally and meshes with the Particulate Matter < 10 um (PM10) air quality measurement and regu­ lation standard. Since 2001, published dust emission estimates for the present climate range from 1000-2150 Tg yr (Table 1). Atmospheric burden estimates range from 8 - 3 6 Tg, an uncertainty factor that e x c e e d s 4. Uncertainty regarding deposition processes which control dust lifetime compound emissions uncertainties to create this surpris­ ingly large discrepancy. It remains unknown how much uncertainty results from insufficient observational constraints and how much arises from different representations among the models of mobilization, transport, and deposition processes [Luo et ai, 2003; Zender et al., 2 0 0 3 ] . This emphasizes the importance of incorpo­ rating observations of dust burden (or proxies such as optical depth) into model estimates (e.g., through assimilation). Few studies pro­ vide quantitative estimates of uncertainties such as natural variability and model error. Including these would more completely rep­ resent, and further widen, the estimated range of mass budgets. In situ observations provide many promising, though underutilized, mass constraints. Zender et al. [2003] selected the best estimate among four different emissions models by minimizing biases against observations of concentration, distribution, and spatial correlation. Further constraints to the minimization problem include the Aerosol Robotic Network (AERONET) data, sediment data, and chemical data. Incorporation of observational constraints (e.g., through assimilation or error minimization techniques) helps reduce propagation of model and mete­ orology uncertainties into mass budget esti­ mates. Making observations available in self-describing software formats with consistent spatial and temporal grids would facilitate this. Satellite observations are more suited to constrain burden than emissions on a global scale. The global dust aerosol burden is nearly proportional to its optical depth, assuming most atmospheric dust mass is carried as optically efficient clay-size particles. Global dust optical depth is now available through satellite retrieval algorithms for incorporation into aerosol assimilation schemes. Such schemes in theory provide better global mass constraints Measurements of dust emission, whereby soil particles enter the atmosphere, are scarce. B e c a u s e no single data set is sufficient to con­ strain the three-dimensional distribution of dust aerosol and its evolution with time, the dust cycle is currently estimated using models that are constrained by available measurements and retrievals. However, a survey of recent models (Table 1) shows that estimates of emission range over more than a factor of 2.This translates into a corresponding uncertainty in the climate effect of dust. One goal of this article is to draw awareness to the wide range of model estimates of dust emission and atmospheric loading. Comparison is made difficult by the different assumptions made by modelers. For example, some models include dust sources created by human dis­ turbance of the soil (by agriculture or overgrazing, for example), while others include only natural sources. Participants at the S e c o n d International Workshop on Mineral Dust, convened in Paris, 10-12 September 2003, agreed that the binary division of dust into natural and anthropogenic emissions is inadequate, and that scientific progress, and the policy which it informs, requires more precise nomenclature. While natural dust sources like deserts are easily visu­ alized, anthropogenic dust sources c o m e in many forms. A consensus terminology is nec­ essary to objectively define, attribute, and intercompare dust mass (and number) budgets and forcings. The s e c o n d goal of this article is to propose a terminology distinguishing different anthro­ pogenic contributions to the dust cycle that is useful for both scientists and policy makers. Distinguishing dust that results from anthro­ pogenic land use c h a n g e (and is potentially remediable) from dust arising from anthropogenic climate change has important consequences for environmental planning. Though dust from all sources combines in the real atmosphere, making provenance difficult to discriminate in observations, it can be distinguished in models. This proposed taxonomy attempts to provide a basis for comparison between models. B Y C . S. ZENDER, R. L . MILLER, AND I.TEGEN than in situ emission or deposition measure­ ments, which are too few for adequate global coverage. Although the retrieved optical thickness summed over all aerosol species remains uncertain to within a factor of 2 [Myhre et al, 2 0 0 4 ] , this is still smaller than the range of dust loading estimated by models. Large-scale transport models have trouble reproducing the transport of large dust parti­ cles measured in remote stations.The Puerto Rico Dust Experiment (PRIDE) campaign (2000) intercompared several size distribution measurement techniques. Dust mass median diameter (MMD) in Barbados was 3.5 ± 1 um based on aerodynamic methods and 9 ± 1 um based on optical methods. During the Inter­ continental Transport and Chemical Transfor­ mation Field Mission in the spring of 2002, the MMD of springtime East Asian dust reaching the west coast of North America was greater than 5 um (K. Perry, personal communication, 2003). Dust MMD in most models is about 2.5 um. The twofold discrepancy between modeled and measured MMD of long range dust implies that model mass budgets may b e systematically biased.This bias manifests as an underestimate of large particle mass and an overestimate of small particle mass. Note that this bias might not be apparent from retrievals of optical depth that are more sensitive to the smaller particles. Proposed Source Classification Models simulate natural and various anthro­ pogenic categories of dust that are difficult or impossible to discriminate in observations. The relative importance of these categories varies regionally and temporally. The aerosol modeling community must first objectively identify and distinguish all useful categories of dust emissions, i.e., natural, anthropogenic, and so forth. A more precise taxonomy will facilitate model intercomparison. Natural Dust. Natural dust is rather intuitive. It is mobilized by wind stress or other natural mechanisms (thermophoresis, electrostatics) over undisturbed source regions. Regions that emitted dust (perhaps intermittently) in the preindustrial era are considered natural sources, although s o m e additional sources have b e e n created since then by nonanthropogenic climate shifts, due to solar forcing or natural variability, for example.The baseline period used to define natural sources should b e explicitly specified, since desert aerosol emis­ sions vary dramatically through the paleocli- mate record. It is r e c o m m e n d e d to reference present climate natural sources to a baseline period ending circa 1750 when C 0 c o n c e n ­ tration was about 280 ppm. Anthropogenic Dust. Anthropogenic dust refers to dust activity (emission and suppres­ sion) that is present due to human activity Humans c a n influence dust emissions in two ways: ( 1 ) by land use which c h a n g e s soil sur­ face conditions, and ( 2 ) by modifying the cli­ mate, which in turn modifies dust emissions. Anthropogenic Dust of the First Kind.This category consists of dust production and emission changes due to human activities that directly modify or disturb the land surface