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Record W4386048488 · doi:10.5194/acp-23-9191-2023

How does tropospheric VOC chemistry affect climate? An investigation of preindustrial control simulations using the Community Earth System Model version 2

2023· article· en· W4386048488 on OpenAlexafffund
Noah A. Stanton, Neil F. Tandon

Bibliographic record

VenueAtmospheric chemistry and physics · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of CanadaAlliance de recherche numérique du CanadaNational Science Foundation
KeywordsTroposphereAtmospheric chemistryTropospheric ozoneAtmospheric sciencesClimate modelAtmosphere (unit)ChemistryAerosolAtmospheric modelsMeteorologyClimatologyEnvironmental scienceClimate changeOzoneGeographyGeologyOceanographyOrganic chemistry

Abstract

fetched live from OpenAlex

Because of their computational expense, models with comprehensive tropospheric chemistry have typically been run with prescribed sea surface temperatures (SSTs), which greatly limits the model's ability to generate climate responses to atmospheric forcings. In the past few years, however, several fully coupled models with comprehensive tropospheric chemistry have been developed. For example, the Community Earth System Model version 2 with the Whole Atmosphere Community Climate Model version 6 as its atmospheric component (CESM2-WACCM6) has implemented fully interactive tropospheric chemistry with 231 chemical species as well as a fully coupled ocean. Earlier versions of this model used a “SOAG scheme” that prescribes bulk emission of a single gas-phase precursor to secondary organic aerosols (SOAs). In contrast, CESM2-WACCM6 simulates the chemistry of a comprehensive range of volatile organic compounds (VOCs) responsible for tropospheric aerosol formation. Such a model offers an opportunity to examine the full climate effects of comprehensive tropospheric chemistry. To examine these effects, 211-year preindustrial control simulations were performed using the following two configurations: (1) the standard CESM2-WACCM6 configuration with interactive chemistry over the whole atmosphere (WACtl) and (2) a simplified CESM2-WACCM6 configuration using a SOAG scheme in the troposphere and interactive chemistry in the middle atmosphere (MACtl). The middle-atmospheric chemistry is the same in both configurations, and only the tropospheric chemistry differs. Differences between WACtl and MACtl were analyzed for various fields. Regional differences in annual mean surface temperature range from −4 to 4 K. In the zonal average, there is widespread tropospheric cooling in the extratropics. Longwave forcers are shown to be unlikely drivers of this cooling, and possible shortwave forcers are explored. Evidence is presented that the climate response is primarily due to increased sulfate aerosols in the extratropical stratosphere and cloud feedbacks. As found in earlier studies, enhanced internal mixing with SOAs in WACtl causes widespread reductions of black carbon (BC) and primary organic matter (POM), which are not directly influenced by VOC chemistry. These BC and POM reductions might further contribute to cooling in the Northern Hemisphere. The extratropical tropospheric cooling results in dynamical changes, such as equatorward shifts of the midlatitude jets, which in turn drive extratropical changes in clouds and precipitation. In the tropical upper troposphere, cloud-driven increases in shortwave heating appear to weaken and expand the Hadley circulation, which in turn drives changes in tropical and subtropical precipitation. Some of the climate responses are quantitatively large enough in some regions to motivate future investigations of VOC chemistry's possible influences on anthropogenic climate change.

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)0.001
metaresearch head score (Gemma)0.002
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.105
Threshold uncertainty score0.209

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.022
GPT teacher head0.222
Teacher spread0.200 · 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
GenreEmpirical

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

Citations2
Published2023
Admission routes2
Has abstractyes

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