Open Fires in Greenland: An Unusual Event and its Impact on the Albedo of the Greenland Ice Sheet
Bibliographic record
Abstract
Abstract. Highly unusual open fires burned in Western Greenland between 31 July and 21 August 2017, after a period of warm, dry and sunny weather. The fires burned on peat lands that became vulnerable to fires by permafrost thawing. We used several satellite data sets to estimate that the total area burned was about 2345 hectares. Based on assumptions of typical burn depths and BC emission factors for peat fires, we estimate that the fires consumed a fuel amount of about 117 kt C and produced BC emissions of about 23.5 t. We used the Lagrangian particle dispersion model to simulate the atmospheric BC transport and deposition. We find that the smoke plumes were often pushed towards the Greenland Ice Sheet by westerly winds and thus a large fraction of the BC emissions (7 t or 30 %) was deposited on snow or ice covered surfaces. The calculated BC deposition was small compared to BC deposition from global sources, but not entirely negligible. Analysis of aerosol optical depth data from three sites in Western Greenland in August 2017 showed strong influence of forest fire plumes from Canada, but little impact of the Greenland fires. Nevertheless, CALIOP lidar data showed that our model captured very effectively the presence and structure of the plume from the Greenland fires. The albedo changes and instantaneous surface radiative forcing in Greenland due to the fire BC emissions were estimated with the SNICAR model and the uvspec model from the libRadtran radiative transfer software package. We estimate that the maximum albedo change due to the BC deposition was about 0.006, too small to be measured by satellites or other means. The average instantaneous surface radiative forcing over Greenland at noon on 31 August was 0.03 W m −2 , with locally occurring maximum values of 0.63 W m −2 . The average value is at least an order of magnitude smaller than the radiative forcing due to BC from other sources. Overall, the fires burning in Greenland in summer of 2017 had little impact on BC deposition on the Greenland Ice Sheet, causing almost negligible extra radiative forcing. This was due to the – in a global context – still rather small size of the fires. However, the very large fraction of the BC emissions deposited on the Greenland Ice Sheet makes these fires very efficient climate forcers on a per unit emission basis. If the expected further warming of Greenland produces much larger fires in the future, this could indeed cause substantial albedo changes and thus lead to accelerated melting of the Greenland Ice Sheet. The fires burning in 2017 may be a harbinger of such future changes.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".