Comparison of seasonal surface temperature trend, spatial variability, and elevation dependency from satellite-derived products and numerical simulations over the Tibetan Plateau from 2003 to 2011
Bibliographic record
Abstract
Land surface temperature (LST) and near-surface air temperature products have been commonly used in the studies of global climate change. In this work, we compare satellite-observed LST from Advanced Along-Track Scanning Radiometer (AATSR) and surface skin temperature and 2 m air temperature (T2) simulated from High Asian Refined analysis (HAR) to provide an independent overview of the surface/air temperature trend over Tibetan Plateau (TP) region in 2003–2011. We investigate the seasonal trend, spatial variability, and the elevation dependency of LST and air temperature over TP for the last decade. Linear regression method is applied to all data sets to illustrate the warming and cooling trends and variability of temperature over the study area. Our analysis shows that an overall warming slope is 0.04 K year–1 in the day trend and 0.05 K year–1 in the night and the trend slope is stronger in the simulated HAR data sets than that in the AATSR LST, especially for the night air temperature. However, in regions with elevation above 4000 m, the proportion of areas with the warming trend is less than 50% except in autumn from HAR data sets. The Namco and Qomolangma sites show an apparent trend of warming and cooling, respectively. The results from both satellite observations and numerical outputs show that warming trend over the entire TP was not obvious during last decade and the cooling trend was even found in the northeast TP.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".