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Record W2324270576 · doi:10.5194/tcd-6-4733-2012

Boreal snow cover variations induced by aerosol emissions in the middle of the 21st century

2012· article· en· W2324270576 on OpenAlexaboutno aff
Martin Ménégoz, Gerhard Krinner, Yves Balkanski, Anne Cozic, Oliviér Boucher, Philippe Ciais

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsnot available
FundersSeventh Framework ProgrammeGrand Équipement National De Calcul IntensifAgence Nationale de la RechercheEuropean Commission
KeywordsSnowAerosolEnvironmental scienceAtmospheric sciencesDeposition (geology)Northern HemisphereClimatologyBorealSnow coverPhysical geographyMeteorologyGeographyGeologyStructural basin

Abstract

fetched live from OpenAlex

Abstract. We used a coupled climate-chemistry model to quantify the impacts of aerosols on snow cover both for the present-day and for the middle of the 21st century. Black carbon (BC) deposition over continents induces a reduction in the Mean Number of Days With Snow at the Surface (MNDWS) that ranges from 0 to 10 days over large areas of Eurasia and Northern America for the present-day relative to the pre-industrial period. This is mainly due to BC deposition during the spring, a period of the year when the remaining of snow accumulated during the winter is exposed to both strong solar radiation and large amount of aerosol deposition induced themselves by a high level of transport of particles from polluted areas. North of 30° N, this deposition flux represents 222 Gg BC month−1 on average from April to June in our simulation. A large reduction in BC emissions is expected in the future in the Radiative Concentration Pathway (RCP) scenarios. Considering this scenario in our simulation leads to a decrease in the spring BC deposition down to 110 Gg month−1 in the 2050s in the RCP8.5 scenario. However, despite the reduction of the aerosol impact on snow, the MNDWS is strongly reduced by 2050, with a decrease ranging from 10 to 100 days from pre-industrial values over large parts of the Northern Hemisphere. This reduction is essentially due to temperature increase, which is quite strong in the RCP8.5 scenario in the absence of climate mitigation policies. Moreover, the projected sea-ice retreat in the next decades will open new routes for shipping in the Arctic. However, a large increase in shipping emissions in the Arctic by the mid 21st century does not lead to significant changes of BC deposition over snow-covered areas in our simulation. Therefore, the MNDWS is clearly not affected through snow darkening effects associated to these Arctic ship emissions. In an experiment without nudging toward atmospheric reanalyses, we simulated however some changes of the MNDWS considering such aerosol ship emissions. These changes are generally not statistically significant in boreal continents, except in the Quebec and in the West Siberian plains, where they range between −5 and −10 days. They are induced both by radiative forcings of the aerosols when they are in the atmosphere, and by all the atmospheric feedbacks. Climate change by the mid 21st century could also cause biomass burning activity (forest fires) to become more intense and occur earlier in the season. In an idealized scenario in which forest fires are 50% stronger and occur 2 weeks earlier than at present, we simulated an increase in spring BC deposition of 21 Gg BC month−1 over continents located north of 30° N. This BC deposition does not impact directly the snow cover through snow darkening effects. However, in an experiment considering all the aerosol forcings and atmospheric feedbacks, enhanced fire activity induces a significant decrease of the MNDWS reaching a dozen of days in Quebec and in Eastern Siberia.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.075
Threshold uncertainty score0.149

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.023
GPT teacher head0.216
Teacher spread0.193 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

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