Arctic spring and summertime aerosol optical depth baseline from long-term observations and model reanalyses – Part 2: Statistics of extreme AOD events, and implications for the impact of regional biomass burning processes
Bibliographic record
Abstract
Abstract. In a companion paper (Xian et al., 2022, part 1 of the study), we present an Arctic aerosol optical depth (AOD) climatology and trend analysis for 2003–2019 spring and summertime periods derived from a combination of aerosol reanalyses, remote-sensing retrievals, and ground observations. Continued from the previous discussion and as the second part of the study, we report the statistics and trends of Arctic AOD extreme events using the U.S. Navy Aerosol Analysis and Prediction System ReAnalysis version 1 (NAAPS-RA v1), the sun photometer data from the AErosol RObotic NETwork (AERONET) sites, and the oceanic Maritime Aerosol Network (MAN) measurements. Here, extreme AOD events are defined as events with AOD exceeding the 95th percentile (denoted “AOD95”) of AOD distributions for given locations using 6-hourly or daily AOD data. While AERONET and MAN data estimate the Arctic median 550 nm AOD value to be 0.07, the 95th percentile value is 0.24. Such extreme events are dominated by fine-mode aerosol particles, largely attributable to biomass burning (BB) smoke events for the North American Arctic, the Asian Arctic, and most areas of the Arctic Ocean. However, extreme AOD events for the lower European Arctic are more attributable to anthropogenic and biogenic fine particles. The extreme-event occurrence dominance of sea salt is largely limited to the North Atlantic and Norwegian Sea. The extreme AOD amplitudes of anthropogenic and biogenic fine-mode and sea salt AOD are, however, significantly lower than those regions where extreme smoke AOD is dominant. Even for sites distant from BB source regions, BB smoke is the principal driver of AOD variation above the AOD95 threshold. Maximum AOD values in the high Arctic in 2010–2019 have increased compared to 2003–2009, indicating stronger extreme BB smoke influence in more recent years. The occurrence of extreme smoke events tended to be more equally distributed over all months (April–August) during the 2003–2009 period while being more concentrated in the late season (July–August) during the 2010–2019 period. The temporal shift of the occurrence of AOD extreme events is likely due to improved control of early-season agriculture burning, climate-change-related increases in summertime lightning frequencies, and a reduction in anthropogenic pollution over the 2010–2019 period.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 source (direct Gemma or distilled Codex), 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".