Temporal and spatial variability of Environmental Lapse Rate distribution over Greenland and the central Arctic from 1980 to 202
Bibliographic record
Abstract
In this study, we derived the environmental lapse rate (ELR) with the new European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data ERA5 that could cover the central Arctic area and an extended period from 1980 to this day. We focus on the Greenland region, where the melting of the Greenland ice sheet plays a vital role in global sea level rise. The temporal and spatial variability of ELR distribution over the Greenland Ice sheet is fully explored in our research and the ELR values distribution over the other central Arctic land area including the Canadian archipelago, high latitude area of North America, and Eurasian are also studied. Our results indicate that ELR values differ dramatically in different seasons and areas, and the commonly used constant ELR −6.5 K/km is not suitable for the Arctic region. The monthly averaged ELR in Greenland shows an annual seasonal cycle with the lowest value is −2.5 K/km in winter. Near-zero ELR occurs in the northeastern marginal part of Greenland for the entire year except summer months. We talked about factors that might cause the near-zero ELR values that occurred over the research area in different seasons and hence research the inversion phenomenon in detail. The freshwater forcing that is equivalent to ice loss from Greenland in the real world is too small to affect the AMOC in climate model experiments. The freshwater flux (FWF) is comprised of runoff(liquid) and discharge(solid). To get a real and complete FWF as a freshwater forcing to activate the hosing experiment, the first step is to downscale near-surface temperature to get a higher-resolution runoff. ELR displays how the temperature near the surface varies with altitude and has been used for downscaling the near-surface temperature which will be further used for obtaining runoff. Our results could not only provide a reference for future near-surface temperature research and studies about inversion phenomena in different regions, but also depict the temperature vertical changes over the Arctic land area with ELR distribution. This research could provide a useful perspective on the changes in the Arctic cryosphere in recent years and should be helpful for a better understanding of mechanisms and feedback that drive the Arctic and subarctic climate changes.
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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 | 0.001 | 0.001 |
| 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".