West Asian climate during the last millennium according to the EC-Earth model
Bibliographic record
Abstract
West Asia is one of the most vulnerable regions to ongoing climate change but has been poorly investigated. Therefore, it is crucial to understand the impact of anthropogenic greenhouse gas, natural forcing, and internal climate variability on temperature and rainfall in this region. In this study, we focus on the climate of West Asia during the last millennium by using a transient simulation of the global earth system model EC-Earth (v3.1). The model performs well in terms of present-day temperature and precipitation patterns and their regional averages. Time series of yearly-mean precipitation and temperature of West Asia show that precipitation increases until the start of the Little Ice Age (1450–1850 CE) and subsequently decreases, whereas temperature shows a cooling trend during the entire last millennium. We first discuss the model output data for climate trends during two periods, 850–1450 CE and 1450–1850 CE. In 850–1450 CE, the largest wetting trend occurred in the eastern regions to the north of the Persian Gulf because of a westward shift of the Indian precipitation core and more moisture transport from the Arabian Sea. The precipitation trend in 1450–1850 CE had a different pattern with a drying trend in the west of the Caspian Sea and overall getting less wet compared with the first period. Temperature showed cooling trends for both periods with the largest values happening in the northern regions. The North Atlantic sea surface temperature cooling and the subsequent change in atmospheric circulation played a role in the wetting and cooling of West Asia. In the second part of the study, we remove the trends and discuss the multi-decadal variability of West Asian climate. It was found that Atlantic multi-decadal and Pacific decadal oscillations strongly contributed to West Asian temperature variability. For West Asian precipitation variability, we found remote connections with the Nordic seas and tropical Pacific Ocean.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".