Towards identifying areas at climatological risk of desertification using the Köppen–Geiger classification and FAO aridity index
Bibliographic record
Abstract
Over the past decades, a continuous rise in global air temperatures resulted in significant changes in the global hydrological cycle. Regionally increased frequencies of extreme weather events and changes in the regional extent of drylands resulted in new areas at risk of desertification, a complex process driven by socio-economic and climate-related factors. Although desertification is not confined to drylands, they are the most vulnerable to land degradation processes. To investigate possible changes in climate patterns over the past 60 years, we couple the information obtained from the Köppen–Geiger (KG) climate classification and the FAO aridity index (AI), providing an overview of the most evident global changes in climate regimes from 1951–1980 to 1981–2010 and focussing on the modifications of the extent of drylands. KG and AI indicators have been computed on a 0.5° × 0.5° global grid using precipitation data from the Full Data Reanalysis (v6.0) of the Global Precipitation Climatology Centre, and mean temperature and potential evapotranspiration data from the Climate Research Unit of the University of East Anglia (CRUTSv3.20). Both KG and AI show that the arid areas globally increased between 1951–1980 and 1981–2010, but decreased on average in the Americas. North-Eastern Brazil, Southern Argentina, the Sahel, Zambia and Zimbabwe, the Mediterranean area, North-Eastern China and Sub-Himalayan India have been identified as areas with a significant increase of drylands extent. An analysis of the scientific literature gives evidence that most of the areas identified are effectively undergoing desertification, thus confirming the validity of AI and KG to highlight the areas under risk of desertification. We also discuss the global decrease of cold areas, the progressive change from continental to temperate climate in Central Europe, the shift from tundra to continental climate in Alaska, Canada and North-Eastern Russia and the widening of the tropical belt.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".