Wildfires in northern Eurasia affect the budget of black carbon in the Arctic – a 12-year retrospective synopsis (2002–2013)
Bibliographic record
Abstract
Abstract. In recent decades much attention has been given to the Arctic environment, where climate change is happening rapidly. Black carbon (BC) has been shown to be a major component of Arctic pollution that also affects the radiative balance. In the present study, we focused on how vegetation fires that occurred in northern Eurasia during the period of 2002–2013 influenced the budget of BC in the Arctic. For simulating the transport of fire emissions from northern Eurasia to the Arctic, we adopted BC fire emission estimates developed independently by GFED3 (Global Fire Emissions Database) and FEI-NE (Fire Emission Inventory – northern Eurasia). Both datasets were based on fire locations and burned areas detected by MODIS (Moderate resolution Imaging Spectroradiometer) instruments on NASA's (National Aeronautics and Space Administration) Terra and Aqua satellites. Anthropogenic sources of BC were adopted from the MACCity (Monitoring Atmospheric Composition and Climate and megacity Zoom for the Environment) emission inventory.During the 12-year period, an average area of 250 000 km2 yr−1 was burned in northern Eurasia (FEI-NE) and the global emissions of BC ranged between 8.0 and 9.5 Tg yr−1 (FEI-NE+MACCity). For the BC emitted in the Northern Hemisphere (based on FEI-NE+MACCity), about 70 % originated from anthropogenic sources and the rest from biomass burning (BB). Using the FEI-NE+MACCity inventory, we found that 102 ± 29 kt yr−1 BC was deposited in the Arctic (defined here as the area north of 67° N) during the 12 years simulated, which was twice as much as when using the MACCity inventory (56 ± 8 kt yr−1). The annual mass of BC deposited in the Arctic from all sources (FEI-NE in northern Eurasia, MACCity elsewhere) is significantly higher by about 37 % in 2009 (78 vs. 57 kt yr−1) to 181 % in 2012 (153 vs. 54 kt yr−1), compared to the BC deposited using just the MACCity emission inventory. Deposition of BC in the Arctic from BB sources in the Northern Hemisphere thus represents 68 % of the BC deposited from all BC sources (the remaining being due to anthropogenic sources). Northern Eurasian vegetation fires (FEI-NE) contributed 85 % (79–91 %) to the BC deposited over the Arctic from all BB sources in the Northern Hemisphere.We estimate that about 46 % of the BC deposited over the Arctic from vegetation fires in northern Eurasia originated from Siberia, 6 % from Kazakhstan, 5 % from Europe, and about 1 % from Mongolia. The remaining 42 % originated from other areas in northern Eurasia. About 42 % of the BC released from northern Eurasian vegetation fires was deposited over the Arctic (annual average: 17 %) during spring and summer.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".