Uncertainties in global future projection of potential evapotranspiration using SSP scenarios
Bibliographic record
Abstract
Evapotranspiration (ET) is the amount of water lost from the global surface, and it represents water and Earth's energy cycle. The intensity and frequency of climate variables have been changed because of the ongoing climate crisis, leading to increased climate disasters, such as heat waves and droughts. The abrupt climate crisis affects the variation of ET because climate variables highly influence ET. However, the future potential ET (PET) estimates include various uncertainty resulted from the variations in the projection of climate variables. In this context, the uncertainty in the projected future PETs should be quantified for the high reliability. Therefore, this study projected future global PET using Penman-Monteith (PM) for the near (2031-2065) and far (2066-2100) futures and quantified the corresponding uncertainty. The six climate variables of 14 CMIP6 GCMs were used for estimating historical PET which were compared to those from the NCEP/NCAR reanalysis data using the five evaluation metrics. The changes in PETs for four Shared Socio-economic Pathways (SSPs) scenarios were calculated for the near and far futures compared to the historical period (1980-2014). Subsequently, the uncertainties of PETs were quantified using the reliability ensemble average method. As a result, the future PET in high latitudes showed the most significant variability compared to the other latitudes. The future PET in the southern hemisphere was higher than the historical PET. Especially the PET in the mid-latitudes of southern hemisphere was the highest among the other latitudes. In addition, the uncertainty of PET was the highest in the high latitudes of the northern hemisphere while the mid-latitude in the northern was the lowest. This study provides insight into evaluating the global water cycle based on PET and helps establish appropriate policies for climate impact assessment.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".