Aryeh Feinberg

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Feinberg

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Aryeh

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0000-0001-5325-4731

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Publications 1 - 10 of 21

Reductions in the deposition of sulfur and selenium to agricultural soils pose risk of future nutrient deficiencies
Feinberg, Aryeh; Stenke, Andrea; Peter, Thomas; et al. (2021)
Communications Earth & Environment
Atmospheric deposition is a major source of the nutrients sulfur and selenium to agricultural soils. Air pollution control and cleaner energy production have reduced anthropogenic emissions of sulfur and selenium, which has led to lower atmospheric deposition fluxes of these elements. Here, we use a global aerosol-chemistry-climate model to map recent (2005–2009) sulfur and selenium deposition, and project future (2095–2099) changes under two socioeconomic scenarios. Across the Northern Hemisphere, we find substantially decreased deposition to agricultural soils, by 70 to 90% for sulfur and by 55 to 80% for selenium. Recent trends in sulfur and selenium concentrations in USA streams suggest that catchment mass balances of these elements are already changing due to the declining atmospheric supply. Sustainable fertilizer management strategies will need to be developed to offset the decrease in atmospheric nutrient supply and ensure future food security and nutrition, while avoiding consequences for downstream aquatic ecosystems.
Atmosphere–ocean–aerosol–chemistry–climate model SOCOLv4.0: description and evaluation
Sukhodolov, Timofei; Egorova, Tatiana; Stenke, Andrea; et al. (2021)
Geoscientific Model Development
This paper features the new atmosphere-ocean-aerosol-chemistry-climate model, SOlar Climate Ozone Links (SOCOL) v4.0, and its validation. The new model was built by interactively coupling the Max Planck Institute Earth System Model version 1.2 (MPI-ESM1.2) (T63, L47) with the chemistry (99 species) and size-resolving (40 bins) sulfate aerosol microphysics modules from the aerosol-chemistry-climate model, SOCOL-AERv2. We evaluate its performance against reanalysis products and observations of atmospheric circulation, temperature, and trace gas distribution, with a focus on stratospheric processes. We show that SOCOLv4.0 captures the low- and midlatitude stratospheric ozone well in terms of the climatological state, variability and evolution. The model provides an accurate representation of climate change, showing a global surface warming trend consistent with observations as well as realistic cooling in the stratosphere caused by greenhouse gas emissions, although, as in previous model versions, a too-fast residual circulation and exaggerated mixing in the surf zone are still present. The stratospheric sulfur budget for moderate volcanic activity is well represented by the model, albeit with slightly underestimated aerosol lifetime after major eruptions. The presence of the interactive ocean and a successful representation of recent climate and ozone layer trends make SOCOLv4.0 ideal for studies devoted to future ozone evolution and effects of greenhouse gases and ozone-destroying substances, as well as the evaluation of potential solar geoengineering measures through sulfur injections. Potential further model improvements could be to increase the vertical resolution, which is expected to allow better meridional transport in the stratosphere, as well as to update the photolysis calculation module and budget of mesospheric odd nitrogen. In summary, this paper demonstrates that SOCOLv4.0 is well suited for applications related to the stratospheric ozone and sulfate aerosol evolution, including its participation in ongoing and future model intercomparison projects.
Modeling the Sulfate Aerosol Evolution After Recent Moderate Volcanic Activity, 2008–2012
Brodowsky, Christina; Sukhodolov, Timofei; Feinberg, Aryeh; et al. (2021)
Journal of Geophysical Research: Atmospheres
Volcanic activity is a major natural climate forcing and an accurate representation of volcanic aerosols in global climate models is essential. This is a complex task involving many uncertainties in the model design and setup and observations. We analyze the performance of the aerosol-chemistry-climate model SOCOL-AERv2 for three medium-sized volcanic eruptions. We focus on the impact of differences in volcanic plume height and SO2 estimates on the stratospheric aerosol burden. The influence of internal model variability and dynamics are addressed through an ensemble of free-running and nudged simulations at different vertical resolutions. Comparing the modeled evolution of the stratospheric aerosol loading to satellite measurements reveals a good model performance. However, a conclusive validation is complicated by uncertainties in observations and emission estimates. The large spread in emitted sulfur amount and its vertical distribution consequently lead to differences in simulated aerosol burdens. Varying tropopause heights among free-running simulations add to these differences, modulating the amount of sulfur injected into the stratosphere. In nudged mode, volcanic aerosol burden peaks are well reproduced, however changes in convection and clouds affect SO2 oxidation paths and cross-tropopause transport, leading to increased background burdens compared to observations. This effect can be reduced by leaving temperatures unconstrained. A higher vertical resolution of 90 levels increases the stratospheric residence time of sulfate aerosol by reducing the diffusion out of the tropical reservoir. We conclude that the model set-up (vertical resolution and free-running vs. nudged) as well as forcing parameters (volcanic emission strength and plume height) contribute equally to the model uncertainties.
The atmospheric sulfur and selenium cycles: a global model of transport and deposition
Feinberg, Aryeh (2020)
Tropospheric ozone in CCMI models and Gaussian process emulation to understand biases in the SOCOLv3 chemistry-climate model
Revell, Laura E.; Stenke, Andrea; Tummon, Fiona; et al. (2018)
Atmospheric Chemistry and Physics
Previous multi-model intercomparisons have shown that chemistry–climate models exhibit significant biases in tropospheric ozone compared with observations. We investigate annual-mean tropospheric column ozone in 15 models participating in the SPARC–IGAC (Stratosphere–troposphere Processes And their Role in Climate–International Global Atmospheric Chemistry) Chemistry-Climate Model Initiative (CCMI). These models exhibit a positive bias, on average, of up to 40%–50% in the Northern Hemisphere compared with observations derived from the Ozone Monitoring Instrument and Microwave Limb Sounder (OMI/MLS), and a negative bias of up to ∼ 30% in the Southern Hemisphere. SOCOLv3.0 (version 3 of the Solar-Climate Ozone Links CCM), which participated in CCMI, simulates global-mean tropospheric ozone columns of 40.2DU – approximately 33% larger than the CCMI multi-model mean. Here we introduce an updated version of SOCOLv3.0, "SOCOLv3.1", which includes an improved treatment of ozone sink processes, and results in a reduction in the tropospheric column ozone bias of up to 8DU, mostly due to the inclusion of N2O5 hydrolysis on tropospheric aerosols. As a result of these developments, tropospheric column ozone amounts simulated by SOCOLv3.1 are comparable with several other CCMI models. We apply Gaussian process emulation and sensitivity analysis to understand the remaining ozone bias in SOCOLv3.1. This shows that ozone precursors (nitrogen oxides (NOx), carbon monoxide, methane and other volatile organic compounds, VOCs) are responsible for more than 90% of the variance in tropospheric ozone. However, it may not be the emissions inventories themselves that result in the bias, but how the emissions are handled in SOCOLv3.1, and we discuss this in the wider context of the other CCMI models. Given that the emissions data set to be used for phase 6 of the Coupled Model Intercomparison Project includes approximately 20% more NOx than the data set used for CCMI, further work is urgently needed to address the challenges of simulating sub-grid processes of importance to tropospheric ozone in the current generation of chemistry–climate models.
Exploring accumulation-mode H2SO4 versus SO2 stratospheric sulfate geoengineering in a sectional aerosol-chemistry-climate model
Vattioni, Sandro; Weisenstein, Debra; Keith, David; et al. (2019)
Atmospheric Chemistry and Physics
Stratospheric sulfate geoengineering (SSG) could contribute to avoiding some of the adverse impacts of climate change. We used the SOCOL-AER global aerosol–chemistry–climate model to investigate 21 different SSG scenarios, each with 1.83 Mt S yr−1 injected either in the form of accumulation-mode H2SO4 droplets (AM H2SO4), gas-phase SO2 or as combinations of both. For most scenarios, the sulfur was continuously emitted at an altitude of 50 hPa (≈20 km) in the tropics and subtropics. We assumed emissions to be zonally and latitudinally symmetric around the Equator. The spread of emissions ranged from 3.75∘ S–3.75∘ N to 30∘ S–30∘ N. In the SO2 emission scenarios, continuous production of tiny nucleation-mode particles results in increased coagulation, which together with gaseous H2SO4 condensation, produces coarse-mode particles. These large particles are less effective for backscattering solar radiation and have a shorter stratospheric residence time than AM H2SO4 particles. On average, the stratospheric aerosol burden and corresponding all-sky shortwave radiative forcing for the AM H2SO4 scenarios are about 37 % larger than for the SO2 scenarios. The simulated stratospheric aerosol burdens show a weak dependence on the latitudinal spread of emissions. Emitting at 30∘ N–30∘ S instead of 10∘ N–10∘ S only decreases stratospheric burdens by about 10 %. This is because a decrease in coagulation and the resulting smaller particle size is roughly balanced by faster removal through stratosphere-to-troposphere transport via tropopause folds. Increasing the injection altitude is also ineffective, although it generates a larger stratospheric burden, because enhanced condensation and/or coagulation leads to larger particles, which are less effective scatterers. In the case of gaseous SO2 emissions, limiting the sulfur injections spatially and temporally in the form of point and pulsed emissions reduces the total global annual nucleation, leading to less coagulation and thus smaller particles with increased stratospheric residence times. Pulse or point emissions of AM H2SO4 have the opposite effect: they decrease the stratospheric aerosol burden by increasing coagulation and only slightly decrease clear-sky radiative forcing. This study shows that direct emission of AM H2SO4 results in higher radiative forcing for the same sulfur equivalent mass injection strength than SO2 emissions, and that the sensitivity to different injection strategies varies for different forms of injected sulfur.
Accelerating models for multiphase chemical kinetics through machine learning with polynomial chaos expansion and neural networks
Berkemeier, Thomas; Krüger, Matteo; Feinberg, Aryeh; et al. (2023)
Geoscientific Model Development
The heterogeneous chemistry of atmospheric aerosols involves multiphase chemical kinetics that can be described by kinetic multi-layer models (KMs) that explicitly resolve mass transport and chemical reactions. However, KMs are computationally too expensive to be used as sub-modules in large-scale atmospheric models, and the computational costs also limit their utility in inverse-modeling approaches commonly used to infer aerosol kinetic parameters from laboratory studies. In this study, we show how machine learning methods can generate inexpensive surrogate models for the kinetic multi-layer model of aerosol surface and bulk chemistry (KM-SUB) to predict reaction times in multiphase chemical systems. We apply and compare two common and openly available methods for the generation of surrogate models, polynomial chaos expansion (PCE) with UQLab and neural networks (NNs) through the Python package Keras. We show that the PCE method is well suited to determining global sensitivity indices of the KMs, and we demonstrate how inverse-modeling applications can be enabled or accelerated with NN-suggested sampling. These qualities make them suitable supporting tools for laboratory work in the interpretation of data and the design of future experiments. Overall, the KM surrogate models investigated in this study are fast, accurate, and robust, which suggests their applicability as sub-modules in large-scale atmospheric models.
Mapping the drivers of uncertainty in atmospheric selenium deposition with global sensitivity analysis
Feinberg, Aryeh; Moustapha, Maliki; Stenke, Andrea; et al. (2020)
Atmospheric Chemistry and Physics
An estimated 0.5–1 billion people globally have inadequate intakes of selenium (Se), due to a lack of bioavailable Se in agricultural soils. Deposition from the atmosphere, especially through precipitation, is an important source of Se to soils. However, very little is known about the atmospheric cycling of Se. It has therefore been difficult to predict how far Se travels in the atmosphere and where it deposits. To answer these questions, we have built the first global atmospheric Se model by implementing Se chemistry in an aerosol–chemistry–climate model, SOCOL-AER (modeling tools for studies of SOlar Climate Ozone Links – aerosol). In the model, we include information from the literature about the emissions, speciation, and chemical transformation of atmospheric Se. Natural processes and anthropogenic activities emit volatile Se compounds, which oxidize quickly and partition to the particulate phase. Our model tracks the transport and deposition of Se in seven gas-phase species and 41 aerosol tracers. However, there are large uncertainties associated with many of the model's input parameters. In order to identify which model uncertainties are the most important for understanding the atmospheric Se cycle, we conducted a global sensitivity analysis with 34 input parameters related to Se chemistry, Se emissions, and the interaction of Se with aerosols. In the first bottom-up estimate of its kind, we have calculated a median global atmospheric lifetime of 4.4 d (days), ranging from 2.9 to 6.4 d (2nd–98th percentile range) given the uncertainties of the input parameters. The uncertainty in the Se lifetime is mainly driven by the uncertainty in the carbonyl selenide (OCSe) oxidation rate and the lack of tropospheric aerosol species other than sulfate aerosols in SOCOL-AER. In contrast to uncertainties in Se lifetime, the uncertainty in deposition flux maps are governed by Se emission factors, with all four Se sources (volcanic, marine biosphere, terrestrial biosphere, and anthropogenic emissions) contributing equally to the uncertainty in deposition over agricultural areas. We evaluated the simulated Se wet deposition fluxes from SOCOL-AER with a compiled database of rainwater Se measurements, since wet deposition contributes around 80 % of total Se deposition. Despite difficulties in comparing a global, coarse-resolution model with local measurements from a range of time periods, past Se wet deposition measurements are within the range of the model's 2nd–98th percentiles at 79 % of background sites. This agreement validates the application of the SOCOL-AER model to identifying regions which are at risk of low atmospheric Se inputs. In order to constrain the uncertainty in Se deposition fluxes over agricultural soils, we should prioritize field campaigns measuring Se emissions, rather than laboratory measurements of Se rate constants.
Emerging investigator series: predicted losses of sulfur and selenium in european soils using machine learning: a call for prudent model interrogation and selection
Jones, Gerrad D.; Insinga, Logan; Droz, Boris; et al. (2024)
Environmental Science: Processes & Impacts
Reductions in sulfur (S) atmospheric deposition in recent decades have been attributed to S deficiencies in crops. Similarly, global soil selenium (Se) concentrations were predicted to drop, particularly in Europe, due to increases in leaching attributed to increases in aridity. Given its international importance in agriculture, reductions of essential elements, including S and Se, in European soils could have important impacts on nutrition and human health. Our objectives were to model current soil S and Se levels in Europe and predict concentration changes for the 21st century. We interrogated four machine-learning (ML) techniques, but after critical evaluation, only outputs for linear support vector regression (Lin-SVR) models for S and Se and the multilayer perceptron model (MLP) for Se were consistent with known mechanisms reported in literature. Other models exhibited overfitting even when differences in training and testing performance were low or non-existent. Furthermore, our results highlight that similarly performing models based on RMSE or R2 can lead to drastically different predictions and conclusions, thus highlighting the need to interrogate machine learning models and to ensure they are consistent with known mechanisms reported in the literature. Both elements exhibited similar spatial patterns with predicted gains in Scandinavia versus losses in the central and Mediterranean regions of Europe, respectively, by the end of the 21st century for an extreme climate scenario. The median change was -5.5% for S (Lin-SVR) and -3.5% (MLP) and -4.0% (Lin-SVR) for Se. For both elements, modeled losses were driven by decreases in soil organic carbon, S and Se atmospheric deposition, and gains were driven by increases in evapotranspiration.
Cosmogenic Isotopes as Proxies for Solar Energetic Particles
Jull, Timothy; Baroni, Mélanie; Feinberg, Aryeh; et al. (2020)
Extreme Solar Particle Storms. The hostile Sun
Since the statistic of solar events based on direct observational data is not sufficient to assess extreme events (see Chapter 2), indirect proxy data needs to be used. The principles and details of the use of cosmogenic isotopes as a proxy for solar energetic particles are presented in this chapter.

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