Diagnostic and model dependent uncertainty of simulated Tibetan permafrost area
Bibliographic record
Abstract
Abstract. We perform a land-surface model intercomparison to investigate how the simulation of permafrost area on the Tibetan Plateau (TP) varies among six modern stand-alone land-surface models (CLM4.5, CoLM, ISBA, JULES, LPJ-GUESS, UVic). We also examine the variability in simulated permafrost area and distribution introduced by five different methods of diagnosing permafrost (from modeled monthly ground temperature, mean annual ground and air temperatures, air and surface frost indexes). There is good agreement (99 to 135 × 104 km2) between the two diagnostic methods based on air temperature which are also consistent with the observation-based estimate of actual permafrost area (101 × 104 km2). However the uncertainty (1 to 128 × 104 km2) using the three methods that require simulation of ground temperature is much greater. Moreover simulated permafrost distribution on the TP is generally only fair to poor for these three methods (diagnosis of permafrost from monthly, and mean annual ground temperature, and surface frost index), while permafrost distribution using air-temperature-based methods is generally good. Model evaluation at field sites highlights specific problems in process simulations likely related to soil texture specification, vegetation types and snow cover. Models are particularly poor at simulating permafrost distribution using the definition that soil temperature remains at or below 0 °C for 24 consecutive months, which requires reliable simulation of both mean annual ground temperatures and seasonal cycle, and hence is relatively demanding. Although models can produce better permafrost maps using mean annual ground temperature and surface frost index, analysis of simulated soil temperature profiles reveals substantial biases. The current generation of land-surface models need to reduce biases in simulated soil temperature profiles before reliable contemporary permafrost maps and predictions of changes in future permafrost distribution can be made for the Tibetan Plateau.
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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) | 0.007 | 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".