Bibliographic record
Abstract
Abstract. The Tibetan Plateau (TP) exerts strong influence on both regional and global climate through thermal and mechanical forcings. The TP also contains the headwaters of large Asian rivers that sustain billions of people and numerous ecosystems. Understanding the characteristics and changes to the hydrological regimes on the TP during the mid-Holocene (MH) will help understand the expected future changes. Here, an analysis of the hydroclimates over the headwater regions of three major rivers originating in the TP, namely the Yellow, Yangtze and Brahmaputra rivers is presented, using an ensemble of climate simulations, which have been dynamically downscaled to 10-km resolution with the Weather Research and Forecasting Model (WRF) coupled to the hydrological model WRF-Hydro. Basin-integrated changes in the seasonal cycle of hydroclimatic variables are considered. In the global model, we have also incorporated Green Sahara (GS) boundary conditions in order to compare with standard MH simulations (which do not include GS) and to capture interactions between the GS and the river hydrographs over the TP. Model-data comparisons show that the dynamically downscaled simulations significantly improve the regional climate simulations over the TP in both the modern day and the MH, highlighting the crucial role of downscaling in both present-day and past climates, although both global and regional models have a cold bias in modern-day simulations and underestimate the wet anomalies inferred from proxy data in the east and southeast part of the TP. TP precipitation is also greatly influenced by the inclusion of a GS, with a particularly large increase predicted over the southern TP, as well as a delay in the monsoon withdrawal. The model performance was first evaluated over the upper basins of the three rivers before the hydrological responses to the MH forcing in streamflow as well as temperature, rainfall and snowmelt for the three basins were quantified via the WRF simulations.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.025 | 0.023 |
| Insufficient payload (model declined to judge) | 0.163 | 0.132 |
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".