Revisiting particle dry deposition and its role in radiative effect estimates

Abstract

Wet and dry deposition remove aerosols from the atmosphere, and these processes control aerosol lifetime and thus impact climate and air quality. Dry deposition is a significant source of aerosol uncertainty in global chemical transport and climate models. Dry deposition parameterizations in most global models were developed when few particle deposition measurements were available. However, new measurement techniques have enabled more size-resolved particle flux observations. We combined literature measurements with data that we collected over a grassland in Oklahoma and a pine forest in Colorado to develop a dry deposition parameterization. We find that relative to observations, previous parameterizations overestimated deposition of the accumulation and Aitken mode particles, and underestimated in the coarse mode. These systematic differences in observed and modeled accumulation mode particle deposition velocities are as large as an order of magnitude over terrestrial ecosystems. As accumulation mode particles form most of the cloud condensation nuclei (CCN) that influence the indirect radiative effect, this model-measurement discrepancy in dry deposition alters modeled CCN and radiative forcing. We present a revised observationally driven parameterization for regional and global aerosol models. Using this revised dry deposition scheme in the Goddard Earth Observing System (GEOS)-Chem chemical transport model, we find that global surface accumulation-mode number concentrations increase by 62% and enhance the global combined anthropogenic and natural aerosol indirect effect by −0.63 W m−2. Our observationally constrained approach should reduce the uncertainty of particle dry deposition in global chemical transport models.

New methodology shows short atmospheric lifetimes of oxidized sulfur and nitrogen due to dry deposition

“…On a regional scale, dry deposition fluxes are typically derived using an inferential approach by multiplying networkmeasured or model-predicted air concentrations with dry deposition velocities (V d ) (Sickles and Shadwick, 2015;Fowler et al, 2009;Meyers et al, 1991), which are derived using resistance-based inferential dry deposition algorithms (Wu et al, 2018) and compared with limited micrometeorological flux measurements (Wesley and Hicks, 2000;Wu et al, 2018;Finkelstein et al, 2000;Matsuda et al, 2006; for validation. When applied to a regional scale, an inferential-algorithm-derived V d may have significant uncertainties (Wesley and Hicks, 2000;Aubinet et al, 2012;Wu et al, 2018;Finkelstein et al, 2000;Matsuda et al, 2006;Makar et al, 2018;Brook et al, 1997). For example, inferred V d for SO 2 , despite being the most studied and best estimated, may be underestimated by 35 % for forest canopies (Finkelstein et al, 2000).…”

Space‐Borne Estimation of Volcanic Sulfate Aerosol Lifetime

“…However, some recent authors have invoked photochemical processes that result in the evaporation of aerosols (Hodzic et al., 2015; Romer et al., 2018; Ye et al., 2017; Zawadowicz et al., 2020). Aerosol deposition rates are thought to depend on size, composition, solubility, and vertical distribution (Croft et al., 2014; Textor et al., 2006), and parameterization algorithms for dry deposition vary by two orders of magnitudes for the same particle size (Emerson et al., 2018, 2020; Saylor et al., 2019). Consequently, dry deposition of fine aerosols is among the largest uncertainties in model estimates of the aerosol indirect forcing (Carslaw et al., 2013).…”

Particle dry deposition algorithms in CMAQ version 5.3: characterization of critical parameters and land use dependence using DepoBoxTool version 1.0

“…atmosphere (Lovett, 1994). The ability of atmospheric models to represent dry deposition processes directly affects the skill with which they can predict particle concentrations with implications for radiative forcing and the role of particles in climate change (Emerson et al, 2020). A previous study from Shu et al (2017) found that dry deposition could cause substantial differences in secondary organic aerosol (SOA) concentrations between two regional chemical transport models (CTMs), the Community Multiscale Air Quality (CMAQ) model, and the Comprehensive Air Quality Model with extensions (CAMx), when accounting for differences in emissions, meteorology, and chemistry.…”

“…However, existing measurements are limited to a few specific land-surface categories (Nemitz et al, 2002). A newly revised particle dry deposition scheme by Emerson et al (2020) could describe observations across a variety of landuse types, suggesting that they have resolved the deficiencies in dry deposition schemes as a result of the lack of many land-use datasets. However, they also pointed out the difficulty in adapting to a sophisticated scheme in CTMs.…”

“…As mentioned above, these considerable uncertainties and differences among mechanistic dry deposition schemes make it difficult to select a "best-performing" scheme for use in CTMs. When higher-accuracy mechanistic dry deposition schemes have been chosen, regional models have incorporated physicochemical properties of particles using a variety of approaches, including representing particle modes with a median particle size and standard deviation (e.g., CMAQ, Binkowski and Shankar (1995)), representing the bulk particle population with a single particle diameter (e.g., CAMx coarse-fine approach), or applying the diameter of discrete size bins (e.g., GEOS-Chem Two-Moment Aerosol Sectional model, Emerson et al (2020)). Saylor et al (2019) found that fineparticle concentration predictions at the surface may vary by 5%-15% depending on the choice of particle deposition velocity schemes in CTMs.…”