CMIP5 permafrost degradation projection:A comparison among different regions
Bibliographic record
Abstract
Abstract The considerable impact of permafrost degradation on hydrology and water resources, ecosystems, human engineering facilities, and climate change requires us to carry out more in‐depth studies, at finer spatial scales, to investigate the issue. In this study, regional differences of the future permafrost changes are explored with respect to the regions (high altitude and high latitude, and in four countries) based on the surface frost index (SFI) model and multimodel and multiscenario data from the fifth phase of the Coupled Model Intercomparison Project (CMIP5). Results show the following: (1) Compared with seven other sets of driving data, Climatic Research Unit air temperature combined with Climate Forecast System Reanalysis snow data (CRU_CFSR) yield a permafrost extent with the least absolute area bias and was thus used in the simulation. The SFI model, driven by CRU_CFSR data climatology plus multimodel mean anomalies, produces a present‐day (1986–2005) permafrost area of 15.45 × 10 6 km 2 decade −1 , which compares reasonably with observations of 15.24 × 10 6 km 2 decade −1 . (2) The high‐altitude (Tibetan Plateau) permafrost area shows a larger decreasing percentage trend than the high‐latitude permafrost area. This indicates that, in terms of speed, high‐altitude permafrost thaw is faster than high‐latitude permafrost, mainly due to the larger percentage sensitivity to rising air temperature of the high‐altitude permafrost compared to the high‐latitude permafrost, which is likely related to their thermal conditions. (3) Permafrost in China shows the fastest thaw, which is reflected by the percentage trend in permafrost area, followed by the United States, Russia, and Canada. These discrepancies are mainly linked to different percentage sensitivities of permafrost areas in these four countries to air temperature change. (4) In terms of the ensemble mean, permafrost areas in all regions are projected to decrease by the period 2080–2099. Under representative concentration pathway (RCP)4.5, permafrost retreats toward the Arctic, and the thaw in every region mainly occurs at the southern edge of the permafrost area. Under RCP8.5, almost no permafrost is expected to remain in China, the United States, and the Tibetan Plateau. Permafrost in Russia will remain mainly in the western part of the east Siberian Mountains, and permafrost in Canada will retreat to the north of 65°N. Possible uncertainties in this study are primarily attributed to the climate model's coarse horizontal resolution. The results of the present study will be useful for understanding future permafrost degradation from the regional perspective.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".