Inclusion of Biomass Burning Plume Injection Height in GEOS‐Chem‐TOMAS: Global‐Scale Implications for Atmospheric Aerosols and Radiative Forcing
Bibliographic record
Abstract
Abstract Aerosols emitted from biomass burning affect human health and climate, both regionally and globally. The magnitude of these impacts is altered by the biomass burning plume injection height (BB‐PIH). However, these alterations are not well‐understood on a global scale. We present the novel implementation of BB‐PIH in global simulations with an atmospheric chemistry model (GEOS‐Chem) coupled with detailed TwO‐Moment Aerosol Sectional (TOMAS) microphysics. We conduct BB‐PIH simulations under three scenarios: (a) All smoke is well‐mixed into the boundary layer, and (b) and (c) smoke injection height is based on Global Fire Assimilation System (GFAS) plume heights. Elevating BB‐PIH increases the simulated global‐mean aerosol optical depth (10%) despite a global‐mean decrease (1%) in near‐surface PM 2.5 . Increasing the tropospheric column mass yields enhanced cooling by the global‐mean clear‐sky biomass burning direct radiative effect. However, increasing BB‐PIH places more smoke above clouds in some regions; thus, the all‐sky biomass burning direct radiative effect has weaker cooling in these regions as a result of increasing the BB‐PIH. Elevating the BB‐PIH increases the simulated global‐mean cloud condensation nuclei concentrations at low‐cloud altitudes, strengthening the global‐mean cooling of the biomass burning aerosol indirect effect with a more than doubling over marine areas. Elevating BB‐PIH also generally improves model agreement with the satellite‐retrieved total and smoke extinction coefficient profiles. Our 2‐year global simulations with new BB‐PIH capability enable understanding of the global‐scale impacts of BB‐PIH modeling on simulated air‐quality and radiative effects, going beyond the current understanding limited to specific biomass burning regions and seasons.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".