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Record W2790344890 · doi:10.1088/1748-9326/aab1b2

The impact of aerosol emissions on the 1.5 °C pathways

2018· article· en· W2790344890 on OpenAlexaff
Anca Hienola, Antti‐Ilari Partanen, Joni‐Pekka Pietikäinen, Declan O’Donnell, Hannele Korhonen, H. Damon Matthews, Ari Laaksonen

Bibliographic record

VenueEnvironmental Research Letters · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsConcordia University
FundersAcademy of Finland
KeywordsAerosolEnvironmental scienceRadiative forcingAtmospheric sciencesContext (archaeology)Greenhouse gasGlobal warmingAir quality indexClimatologyClimate modelClimate changeMeteorologyGeographyPhysicsEcology

Abstract

fetched live from OpenAlex

To assess the impact of anthropogenic aerosol emission reduction on limiting global temperature increase to 1.5 °C or 2 °C above pre-industrial levels, two climate modeling approaches have been used (MAGICC6, and a combination of ECHAM-HAMMOZ and the UVic ESCM), with two aerosol control pathways under two greenhouse gas (GHG) reduction scenarios. We found that aerosol emission reductions associated with CO _2 co-emissions had a significant warming effect during the first half of the century and that the near-term warming is dependent on the pace of aerosol emission reduction. The modeling results show that these aerosol emission reductions account for about 0.5 °C warming relative to 2015, on top of the 1 °C above pre-industrial levels that were already reached in 2015. We found also that the decreases in aerosol emissions lead to different decreases in the magnitude of the aerosol radiative forcing in the two models. By 2100, the aerosol forcing is projected by ECHAM–UVic to diminish in magnitude by 0.96 W m ^−2 and by MAGICC6 by 0.76 W m ^−2 relative to 2000. Despite this discrepancy, the climate responses in terms of temperature are similar. Aggressive aerosol control due to air quality legislation affects the peak temperature, which is 0.2 °C–0.3 °C above the 1.5 °C limit even within the most ambitious CO _2 /GHG reduction scenario. At the end of the century, the temperature differences between aerosol reduction scenarios in the context of ambitious CO _2 mitigation are negligible.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.518
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.041
GPT teacher head0.284
Teacher spread0.244 · 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 teacher head, not a consensus.

Study designBench or experimental
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

Citations37
Published2018
Admission routes1
Has abstractyes

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