Uncertainty Assessments of Multi-GCM, Multi-Scenario, and Multi-Factor for Temperature Projections: an Integrated SCA-WME-MFA Method
Bibliographic record
Abstract
Assessing the impacts of multiple sources on statistical downscaling is challenged by uncertainty from global climate model (GCM), scenario and factor. In our study, by integrating stepwise cluster analysis (SCA), wavelet-based multiscale entropy (WME), and multi-level factorial analysis (MFA); a SCA-WME-MFA is developed to quantitatively analyze the diverse uncertainty (i.e., numerical fluctuation, and the complexity of the modes) of daily mean temperatures (Tmean) for Amu Darya River Basin (ADRB). The major results reveal that: (i) the most remarkable warming rate would be obtained (0.056 ± 0.015 ◦C/year) under SSP5-8.5; (ii) Compared to the base period (1979–2005), Tmean under SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 would increase by 1.06 ± 1.26 ◦C,1.38 ± 1.39 ◦C, 1.741 ± 1.255 ◦C, and 2.05 ± 1.22 ◦C in the future (2022-2097); (iii) the secular mode of temperature projections is complex (WME values = 0.81 ± 0.15), while the short-term mode is relatively single (WME values = 0.14 ± 0.13); (iv), the uncertainty of temperature projections would increase under the resource and energy intensive development scenario SSP5-8.5; (v) the annual scales features of temperature projections has a marked impact on the relationships between Tmean and factors, and they can be identified by SCA model; (vi) air temperature at 850 hPa has dominant effect on the numerical fluctuation, and the interactions of geopotential height at 500 hPa on other factors have significant effects on downscaling processes; (vii) the ensemble downscaling based on multi-GCM datasets can reduce the diverse uncertainty of temperature projections.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".