MétaCan
Menu
Back to cohort
Record W4404114873 · doi:10.5194/gmd-17-7767-2024

A fully coupled solid-particle microphysics scheme for stratospheric aerosol injections within the aerosol–chemistry–climate model SOCOL-AERv2

2024· article· en· W4404114873 on OpenAlexaff
Sandro Vattioni, Rahel Weber, Aryeh Feinberg, Andrea Stenke, J. A. Dykema, Beiping Luo, Georgios A. Kelesidis, Christian A. Bruun, Timofei Sukhodolov, Frank N. Keutsch, Thomas Peter, Gabriel Chiodo

Bibliographic record

VenueGeoscientific model development · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Geoengineering
Canadian institutionsInstitute of Particle Physics
FundersEidgenössische Technische Hochschule ZürichScience Foundation IrelandSimons FoundationSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsAerosolAtmospheric sciencesEnvironmental scienceMeteorologyParticle (ecology)StratosphereClimate modelAtmospheric chemistryClimatologyPhysicsOzoneClimate changeGeologyOceanography

Abstract

fetched live from OpenAlex

Abstract. Recent studies have suggested that injection of solid particles such as alumina and calcite particles for stratospheric aerosol injection (SAI) instead of sulfur-based injections could reduce some of the adverse side effects of SAI such as ozone depletion and stratospheric heating. Here, we present a version of the global aerosol–chemistry–climate model SOCOL-AERv2 and the Earth system model (ESM) SOCOLv4 which incorporate a solid-particle microphysics scheme for assessment of SAI of solid particles. Microphysical interactions of the solid particle with the stratospheric sulfur cycle were interactively coupled to the heterogeneous chemistry scheme and the radiative transfer code (RTC) for the first time within an ESM. Therefore, the model allows simulation of heterogeneous chemistry at the particle surface as well as feedbacks between microphysics, chemistry, radiation and climate. We show that sulfur-based SAI results in a doubling of the stratospheric aerosol burden compared to the same mass injection rate of calcite and alumina particles with a radius of 240 nm. Most of the sulfuric acid aerosol mass resulting from SO2 injection does not need to be lifted to the stratosphere but is formed after in situ oxidation and subsequent water uptake in the stratosphere. Therefore, to achieve the same radiative forcing, larger injection rates are needed for calcite and alumina particle injection than for sulfur-based SAI. The stratospheric sulfur cycle would be significantly perturbed, with a reduction in stratospheric sulfuric acid burden by 53 %, when injecting 5 Mt yr−1 (megatons per year) of alumina or calcite particles of 240 nm radius. We show that alumina particles will acquire a sulfuric acid coating equivalent to about 10 nm thickness if the sulfuric acid is equally distributed over the whole available particle surface area in the lower stratosphere. However, due to the steep contact angle of sulfuric acid on alumina particles, the sulfuric acid coating would likely not cover the entire alumina surface, which would result in available surface for heterogeneous reactions other than the ones on sulfuric acid. When applying realistic uptake coefficients of 1.0, 10−5 and 10−4 for H2SO4, HCl and HNO3, respectively, the same scenario with injections of calcite particles results in 94 % of the particle mass remaining in the form of CaCO3. This likely keeps the optical properties of the calcite particles intact but could significantly alter the heterogeneous reactions occurring on the particle surfaces. The major process uncertainties of solid-particle SAI are (1) the solid-particle microphysics in the injection plume and degree of agglomeration of solid particles on the sub-ESM grid scale, (2) the scattering properties of the resulting agglomerates, (3) heterogeneous chemistry on the particle surface, and (4) aerosol–cloud interactions. These uncertainties can only be addressed with extensive, coordinated experimental and modelling research efforts. The model presented in this work offers a useful tool for sensitivity studies and incorporating new experimental results on SAI of solid particles.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.040
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.025
GPT teacher head0.249
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations10
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueGeoscientific model developmentSame topicClimate Change and GeoengineeringFrench-language works237,207