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Record W2021822583 · doi:10.1029/2007jd009657

Radiative impact of boreal smoke in the Arctic: Observed and modeled

2008· article· en· W2021822583 on OpenAlexaboutno aff
Robert S. Stone, Gail P. Anderson, E. P. Shettle, Elisabeth Andrews, K. Loukachine, Ellsworth G Dutton, Crystal Schaaf, Miguel O. Román

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2008
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental scienceAtmospheric sciencesAtmosphere (unit)LongwaveShortwaveEarth's energy budgetRadiative transferArcticRadianceAerosolIrradianceShortwave radiationAtmospheric radiative transfer codesRadiative forcingMeteorologyRemote sensingRadiationPhysicsGeology

Abstract

fetched live from OpenAlex

The Arctic climate is modulated, in part, by the presence of aerosols that affect the horizontal and vertical distribution of radiant energy passing through the atmosphere. Aerosols affect the surface‐atmosphere radiation balance directly through interactions with solar and terrestrial radiation and indirectly through interactions with cloud particles. During summer 2004 forest fires destroyed vast areas of boreal forest in Alaska and western Canada, releasing smoke into the atmosphere. Smoke aerosol passing over instrumented field sites near Barrow, Alaska, was monitored to determine its physical and optical properties and its impact on the surface radiation budget. Empirical determinations of the direct aerosol radiative forcing (DARF) by the smoke were used to corroborate simulations made using the Moderate Resolution Transmittance radiative transfer model, MODTRAN™5. DARF is defined as the change in net shortwave irradiance per unit of aerosol optical depth (AOD). DARF, varying with solar angle and surface type, was evaluated at the surface, at the top of the atmosphere (TOA), and within the intervening layers of the atmosphere. The TOA results are compared with fluxes derived from coincident satellite retrievals made using the Clouds and the Earth's Radiant Energy System (CERES) radiance data. Smoke tends to reduce the net shortwave irradiance at the surface while increasing it within layers in which it resides. Over the Arctic tundra during summer, a layer of smoke having AOD = 0.5 at 500 nm produces a diurnally averaged DARF of about −40 W m−2 at the surface and −20 W m−2 at TOA, while the layer itself tends to warm at a rate of ≈1 K d−1. The tendency of smoke to cool the surface while heating the layer above may lead to increased atmospheric stability and suppress cloud formation. Radiative forcing at the top of the atmosphere is especially sensitive to small changes in surface albedo, evidenced in both the model results and satellite retrievals. TOA net shortwave flux decreases when smoke is present over dark surfaces and tends to increase if the underlying surface is bright. For example, at solar noon during midsummer at Barrow, a layer of smoke having AOD(500) = 0.5 will reduce the net shortwave flux at TOA by ≈30 W m−2 over the ocean while at the same time increasing it by 20 W m−2 over an adjacent area of melting sea ice. For smoke aerosol, the sensitivity of DARF to changing surface albedo (assuming a solar zenith angle of 50°) is about +15 W m−2 AOD−1 for every increase in surface albedo of 0.10. Throughout the Arctic summer, surface and TOA cooling and a tendency toward warming in the intervening atmospheric layers are the dominant radiative impacts of boreal smoke over the ocean and tundra areas, but the radiative forcing at TOA is positive over regions covered by ice or snow. Enhanced differential cooling/heating of ocean, ice, and snow due to the presence of smoke in the atmosphere may affect regional circulation patterns by perturbing diabatic processes. Should the frequency and intensity of boreal fires increase in the future because of global warming, the more persistent presence of smoke in the atmosphere may be manifest as a negative feedback at the surface. In addition, there will likely be indirect radiative impacts of the smoke as it influences cloudiness, which in turn further modulates the Arctic radiation budget.

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)0.001
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 score0.249
Threshold uncertainty score0.495

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.084
GPT teacher head0.325
Teacher spread0.240 · 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

Citations100
Published2008
Admission routes1
Has abstractyes

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