Heterogeneous impact of dust on tropospheric ozone: Sensitivity to season, species, and uptake rates

Abstract. Heterogeneous chemistry on mineral dust particles causes significant reductions in important tropospheric trace gases such as O 3 , OH, and HNO 3 in dust-dominated regions such as the North African Tropical Atlantic region. We analyze the spatial and temporal modes of dust-induced heterogeneous ozone removal (∆ H O 3 ) using empirical orthogonal functions (EOFs) and principal components analysis. We use the results to attribute ozone removal to specific pathways, and to assess the sensitivity of ozone removal to uncertainties in key heterogeneous uptake rates. The first EOF mode dominates ∆ H O 3 variance (93%) and shows that dust reduces O 3 through heterogeneous reactions globally and year-around with the maximum in July. The second mode explains only 4% of ∆ H O 3 spatial variance yet accounts for most ∆ H O 3 seasonality. With best-guess uptake coefficients, indirect ozone reduction due to HNO 3 uptake exceeds direct heterogeneous uptake of O 3 . However, uncertainties in uptake rates allow the possibility that direct O 3 uptake exceeds HNO 3 -induced O 3 uptake, especially in Northern Spring. Recently published HNO 3 uptake coefficients on authentic dust range from 10 −5 < γ HNO 3 < 0.2, and imply that dust destroys 0.5-5.2% of tropospheric O 3 , respectively. Improved γ HNO 3 measurements and correct model representation of global dust composition, deliquesence, and aging are required to further reduce these order-of-magnitude uncertainties

Global estimates of mineral dust aerosol iron and aluminum solubility that account for particle size using diffusion-controlled and surface-area-controlled approximations

Mineral aerosol deposition is recognized as the dominant source of iron to the open ocean and the solubility of iron in the dust aerosol is highly variable, with measurements ranging from 0.01–80%. Global models have difficulty capturing the observed variations in solubility, and have ignored the solubility dependence on aerosol size. We introduce two idealized physical models to estimate the size dependence of mineral aerosol solubility: a diffusion‐controlled model and a surface‐area‐controlled model. These models produce differing time‐ and space‐varying solubility maps for aerosol Fe and Al given the dust age at deposition, size‐resolved dust entrainment fields, and the aerosol acidity. The resulting soluble iron deposition fluxes are substantially different, and more realistic, than a globally uniform solubility approximation. The surface‐area‐controlled solubility varies more than the diffusion‐controlled solubility and better captures the spatial pattern of observed solubility in the Atlantic. However, neither of these two models explains the large solubility variation observed in the Pacific. We then examine the impacts of spatially variable, size‐dependent solubility on marine biogeochemistry with the Biogeochemical Elemental Cycling (BEC) ocean model by comparing the modeled surface ocean dissolved Fe and Al with observations. The diffusion‐based variable solubility does not significantly improve the simulation of dissolved Fe relative to a 5% globally uniform solubility, while the surface‐area‐based variable solubility improves the simulation in the North Atlantic but worsens it in the Pacific and Indian Oceans

Present‐day climate forcing and response from black carbon in snow,

[1] We apply our Snow, Ice, and Aerosol Radiative (SNICAR) model, coupled to a general circulation model with prognostic carbon aerosol transport, to improve understanding of climate forcing and response from black carbon (BC) in snow. Building on two previous studies, we account for interannually varying biomass burning BC emissions, snow aging, and aerosol scavenging by snow meltwater. We assess uncertainty in forcing estimates from these factors, as well as BC optical properties and snow cover fraction. BC emissions are the largest source of uncertainty, followed by snow aging. The rate of snow aging determines snowpack effective radius (r e ), which directly controls snow reflectance and the magnitude of albedo change caused by BC. For a reasonable r e range, reflectance reduction from BC varies threefold. Inefficient meltwater scavenging keeps hydrophobic impurities near the surface during melt and enhances forcing. Applying biomass burning BC emission inventories for a strong (1998) and weak Citation: Flanner, M. G., C. S. Zender, J. T. Randerson, and P. J. Rasch (2007), Present-day climate forcing and response from black carbon in snow

Satellites reveal Earth's seasonally shifting dust emission sources

Establishing mineral dust impacts on Earth's systems requires numerical models of the dust cycle. Differences between dust optical depth (DOD) measurements and modelling the cycle of dust emission, atmospheric transport, and deposition of dust indicate large model uncertainty due partially to unrealistic model assumptions about dust emission frequency. Calibrating dust cycle models to DOD measurements typically in North Africa, are routinely used to reduce dust model magnitude. This calibration forces modelled dust emissions to match atmospheric DOD but may hide the correct magnitude and frequency of dust emission events at source, compensating biases in other modelled processes of the dust cycle. Therefore, it is essential to improve physically based dust emission modules. Here we use a global collation of satellite observations from previous studies of dust emission point source (DPS) dichotomous frequency data. We show that these DPS data have little-to-no relation with MODIS DOD frequency. We calibrate the albedo-based dust emission model using the frequency distribution of those DPS data. The global dust emission uncertainty constrained by DPS data (±3.8 kg m−2 y−1) provides a benchmark for dust emission model development. Our calibrated model results reveal much less global dust emission (29.1 ± 14.9 Tg y−1) than previous estimates, and show seasonally shifting dust emission predominance within and between hemispheres, as opposed to a persistent North African dust emission primacy widely interpreted from DOD measurements. Earth's largest dust emissions, proceed seasonally from East Asian deserts in boreal spring, to Middle Eastern and North African deserts in boreal summer and then Australian shrublands in boreal autumn-winter. This new analysis of dust emissions, from global sources of varying geochemical properties, have far-reaching implications for current and future dust-climate effects. For more reliable coupled representation of dust-climate projections, our findings suggest the need to re-evaluate dust cycle modelling and benefit from the albedo-based parameterisation

Elucidating Hidden and Enduring Weaknesses in Dust Emission Modeling

Large-scale classical dust cycle models, developed more than two decades ago, assume for simplicity that the Earth's land surface is devoid of vegetation, reduce dust emission estimates using a vegetation cover complement, and calibrate estimates to observed atmospheric dust optical depth (DOD). Consequently, these models are expected to be valid for use with dust-climate projections in Earth System Models. We reveal little spatial relation between DOD frequency and satellite observed dust emission from point sources (DPS) and a difference of up to 2 orders of magnitude. We compared DPS data to an exemplar traditional dust emission model (TEM) and the albedo-based dust emission model (AEM) which represents aerodynamic roughness over space and time. Both models overestimated dust emission probability but showed strong spatial relations to DPS, suitable for calibration. Relative to the AEM calibrated to the DPS, the TEM overestimated large dust emission over vast vegetated areas and produced considerable false change in dust emission. It is difficult to avoid the conclusion that calibrating dust cycle models to DOD has hidden for more than two decades, these TEM modeling weaknesses. The AEM overcomes these weaknesses without using masks or vegetation cover data. Considerable potential therefore exists for ESMs driven by prognostic albedo, to reveal new insights of aerosol effects on, and responses to, contemporary and environmental change projections

Data-Driven Artificial Intelligence for Calibration of Hyperspectral Big Data

Near-earth hyperspectral big data present both huge opportunities and challenges for spurring developments in agriculture and high-throughput plant phenotyping and breeding. In this article, we present data-driven approaches to address the calibration challenges for utilizing near-earth hyperspectral data for agriculture. A data-driven, fully automated calibration workflow that includes a suite of robust algorithms for radiometric calibration, bidirectional reflectance distribution function (BRDF) correction and reflectance normalization, soil and shadow masking, and image quality assessments was developed. An empirical method that utilizes predetermined models between camera photon counts (digital numbers) and downwelling irradiance measurements for each spectral band was established to perform radiometric calibration. A kernel-driven semiempirical BRDF correction method based on the Ross Thick-Li Sparse (RTLS) model was used to normalize the data for both changes in solar elevation and sensor view angle differences attributed to pixel location within the field of view. Following rigorous radiometric and BRDF corrections, novel rule-based methods were developed to conduct automatic soil removal; and a newly proposed approach was used for image quality assessment; additionally, shadow masking and plot-level feature extraction were carried out. Our results show that the automated calibration, processing, storage, and analysis pipeline developed in this work can effectively handle massive amounts of hyperspectral data and address the urgent challenges related to the production of sustainable bioenergy and food crops, targeting methods to accelerate plant breeding for improving yield and biomass traits