Tropical semi-arid regions expanding over temperate latitudes under climate change
Bibliographic record
Abstract
Highly populated, water-limited and warm drylands are challenging areas for development and are expected to expand overall under several scenarios of climate change. Here, we adopt a bioclimatic approach based on the Köppen classification to focus on the evolution of warm semi-arid regions over the projected twenty-first century, following three socio-economic scenarios and 12 global climate models from the last IPCC exercise (CMIP5). We show that a global expansion of this climatic domain has already started according to climate observations in the twentieth century (about + 13% of surface increase, i.e. from 6 to 7% of the global land surface). Models project that this expansion will continue throughout the twenty-first century, whatever the scenario: for the most dramatic one (RCP 8.5), the share of the total land surface occupied by warm semi-arid surfaces is about 38% higher in 2100 compared to the present (from ∼ 7 to ∼ 9% of the global land surface). This expansion will essentially take place outside of the tropical belt, showing a poleward migration as large as 11 ∘ of latitude in the Northern Hemisphere. This expansion is linearly correlated with the projected future global warming (about 853 millions km 2 per degree of warming for RCP 8.5). Different types of climate class transitions and their associated mechanisms are discussed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".