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Record W4232895895 · doi:10.5194/acp-2019-589

How emissions uncertainty influences the distribution and radiative impacts of smoke from fires in North America

2019· preprint· en· W4232895895 on OpenAlexaboutno aff
Therese S. Carter, Colette L. Heald, J. L. Jiménez, Pedro Campuzano‐Jost, Y. Kondo, Nobuhiro Moteki, Joshua P. Schwarz, Christine Wiedinmyer, Anton Darmenov, Arlindo da Silva, Johannes W. Kaiser

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
FundersGlobal Foundation for Eating DisordersNational Oceanic and Atmospheric AdministrationDesert Research InstituteNational Park ServiceUniversity of California, DavisU.S. Environmental Protection Agency
KeywordsEnvironmental scienceBiomass burningAerosolChemical transport modelAir quality indexAtmospheric sciencesRadiative transferSmokeMeteorologyClimatologyGeography

Abstract

fetched live from OpenAlex

Abstract. Fires and the aerosols that they emit impact air quality, health, and climate, but the abundance and properties of carbonaceous aerosol (both black carbon and organic carbon) from biomass burning (BB) remain uncertain and poorly constrained. We aim to quantify the uncertainties associated with fire emissions and their air quality and radiative impacts from underlying dry matter consumed and emissions factors. To explore this, we compare model simulations from a global chemical transport model, GEOS-Chem, driven by a variety of fire emission inventories with surface and airborne observations of black carbon (BC) and organic aerosol (OA) concentrations and satellite-derived aerosol optical depth (AOD). We focus on two fire detection/burned area-based (FD/BA) inventories using burned area and active fire counts, respectively: the Global Fire Emissions Database version 4 (GFED4s) with small fires and the Fire INventory from NCAR version 1.5 (FINN1.5) and two fire radiative power (FRP)-based approaches: the Quick Fire Emission Dataset version 2.4 (QFED2.4) and the Global Fire Assimilation System version 1.2 (GFAS1.2). We show that, across the inventories, emissions of BB aerosol (BBA) differ by a factor of 4 to 7 over North America and that dry matter differences, not emissions factors, drive this spread. We find that simulations driven by QFED2.4 generally overestimate BC and, to a lesser extent, OA concentrations observations from two fire-influenced aircraft campaigns in North America (ARCTAS and DC3) and from the Interagency Monitoring of Protected Visual Environments (IMPROVE) network, while simulations driven by FINN1.5 substantially underestimate concentrations. The GFED4s and GFAS1.2-driven simulations provide the best agreement with OA and BC mass concentrations at the surface (IMPROVE), BC observed aloft (DC3 and ARCTAS), and AOD observed by MODIS over North America. We also show that a sensitivity simulation including an enhanced source of secondary organic aerosol (SOA) from fires based on the NOAA Fire Lab 2016 experiments produces substantial additional OA; however, the spread in the primary emissions estimates implies that this magnitude of SOA cannot be either confirmed or ruled out when comparing the simulations against the observations explored here. Given the substantial uncertainty in fire emissions, as represented by these four emission inventories, we find a sizeable range in BBA population-weighted exposure over Canada and the contiguous United States (0.5 to 1.6 µg m−3). We also show that the range in the estimated global direct radiative effect of carbonaceous aerosol from fires (−0.11 to −0.048 W m−2) is large and comparable to the direct radiative forcing of OA (−0.09 W m−2) estimated in AR5. Our analysis suggests that fire emissions uncertainty challenges our ability to accurately characterize the impact of smoke on air quality and climate.

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 score0.826
Threshold uncertainty score0.346

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
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 science0.0010.001
Research integrity0.0010.001
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 head0.009
GPT teacher head0.229
Teacher spread0.220 · 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

Citations17
Published2019
Admission routes1
Has abstractyes

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