Strengthening atmospheric greenhouse effect in the Arctic evidenced by long-term records of downwelling longwave radiance spectrum
Bibliographic record
Abstract
Arctic Amplification, the accelerated warming of the Arctic compared to lower latitude, remains poorly understood, particularly regarding the role of clouds. In this study, we analyze 26 years of Downwelling Longwave Radiation (DLR) measurements from an Extended-range Atmospheric Emitted Radiance Interferometer (E-AERI) at the U.S. Department of Energy's North Slope of Alaska site. Our analysis reveals a pan-spectral increase in DLR under all sky conditions, primarily driven by an enhanced greenhouse effect from thick/low clouds and greenhouse gases. We find that the observed DLR trends are primarily driven by radiance changes within individual sky conditions, rather than changes in cloud fraction, suggesting fundamental shifts in atmospheric emissivity and/or temperature. Comparative analysis between the Southern Great Plains (mid-latitude) and North Slope of Alaska (high-latitude) sites demonstrates that clouds impact the longwave surface energy balance differently between the two locations. In the Arctic, the pan-spectral increase in DLR trend dampens out the radiative cooling of the warming surface, contributing significantly to Arctic Amplification. Consistent positive trends are found in the far-infrared, a spectral region sensitive to even small changes of the dry and cold Arctic’s atmosphere and of significant interest in the current warming context. Additionally, we observe a relatively weaker DLR trend in the ozone absorption band under every sky condition, indicating a tropospheric ozone radiative forcing in the Arctic climate.
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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 | 0.000 | 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 | 0.000 |
| 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 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".