Bibliographic record
Abstract
We present an Arctic aerosol optical depth (AOD) climatology and trend analysis for 2003–2019 spring and summertime periods derived from a combination of multi-agency aerosol reanalyses, remote sensing retrievals, and ground observations. This includes the U.S. Navy Aerosol Analysis and Prediction System ReAnalysis version 1 (NAAPS-RA v1), the NASA Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), and the Copernicus Atmosphere Monitoring Service ReAnalysis (CAMSRA). Space-borne remote sensing retrievals of AOD are considered from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Multi-angle Imaging SpectroRadiometer (MISR), and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). Ground-based data include sun photometer data from Aerosol Robotic Network (AERONET) sites and oceanic Maritime Aerosol Network (MAN) measurements. Aerosol reanalysis AODs and space-borne retrievals show consistent climatological spatial patterns and trends for both spring and summer seasons over the sub-Arctic (60–70° N). Consistent signs in the AOD trend are also found for the high Arctic (north of 70° N) from reanalyses. The aerosol reanalyses yield more consistent AOD results than climate models, verify well with AERONET, and corroborate complementary climatological and trend analysis. Speciated AODs are more variable than total AOD among the three reanalyses, and a little more so for March–May (MAM) than for June–August (JJA). Black Carbon (BC) AOD in the Arctic comes predominantly from biomass burning sources in both MAM and JJA, and biomass burning overwhelms anthropogenic sources in JJA for the study period. AOD exhibits a negative trend in the Arctic in MAM, and a positive trend in JJA during 2003–2019, due to an overall decrease in sulfate/anthropogenic pollutions, and a significant increase in biomass burning smoke in JJA. Interannual Arctic AOD variability is significantly large, driven by fine-mode, and specifically, biomass burning (BB) smoke, though more so in JJA than in MAM. Extreme AOD events during spring and summer in the Arctic, defined as AOD greater than the 95th percentile value, are mainly attributed to BB smoke transport events. Extreme AOD cases tend to occur later in the season (i.e., July and August, in the latter decade rather than spreading over April–August in the early decade during 2003–2019) corresponding to a shift to a later time in extreme boreal BB activities.
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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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.013 | 0.014 |
| Insufficient payload (model declined to judge) | 0.256 | 0.224 |
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".