Regional and seasonal variability of clouds in relation with environmental parameters in the Arctic region based on spaceborne remote sensing
Bibliographic record
Abstract
<!--!introduction!--> The Arctic region is known to be the most sensitive to climate change. This area is warming two to three times faster than other regions of the world. Clouds represent one of the largest sources of uncertainty in modelling the Arctic response to climate change. It is therefore essential to study the impacts and changes related to the thermodynamic and cloud conditions of the area. In the present work, low-level clouds (below 3000 m) occurrences are investigated within the studied area. We analyze their spatial and seasonal variability over the whole Arctic region. This study is conducted between 60°N and 82°N using active remote sensing observations from CALIPSO/CloudSat satellites. The lidar/radar synergy (DARDAR products) allows to identify cloud phase and to determine cloud occurrences. This unique dataset allows the investigation of cloud variability and its influencing parameters over the whole Arctic region and over several years (2007-2016). Statistical analyses (ACP, multilinear regression and clustering) are used to highlight the influencing parameters of the local and global cloud occurrences. We will present results on time and space variability of clouds over the entire Arctic, and over specific regions. The impact of thermodynamic parameters, sea ice concentration and coupling with surface on cloud phase and occurrence are investigated. On a regional scale, cloud occurrences seem to be correlated with surface conditions. The surface temperature as well as the Lower Tropospheric Stability (LTS) seem to be the predominant influencing parameters. The regionalization shows more pronounced results and trends than for the entire region.
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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".