Updated Trewartha climate classification with four climate change scenarios
Bibliographic record
Abstract
Abstract The Updated Trewartha climate classification (TWCC) at global level shows the changes that are expected as a consequence of global temperature increase and imbalance of precipitation. This type of classification is more precise than the Köppen climate classification. Predictions included the increase in global temperature (T in °C) and change in the amount of precipitation (PA in mm). Two climate models MIROC6 and IPSL‐CM6A‐ LR were used, along with 4261 meteorological stations from which the data on temperature and precipitation were taken. These climate models were used because they represent the most extreme models in the CMIP6 database. Four scenarios of climate change and their territories were analysed in accordance with the TWCC classification. Four scenarios of representative concentration pathway (RCP) by 2.6, 4.5, 6.0 and 8.5 W/m 2 follow the increase of temperature between 0.3°C and 4.3°C in relation to precipitation and are being analysed for the periods 2021–2040, 2041–2060, 2061–2080 and 2081–2100. The biggest extremes are shown in the last grid for the period 2081–2100, reflecting the increase of T up to 4.3°C. With the help of GIS (geographical information systems) and spatial analyses, it is possible to estimate the changes in climate zones as well as their movement. Australia and South East Asia will suffer the biggest changes of biomes, followed by South America and North America. Climate belts to undergo the biggest change due to such temperature according to TWCC are Ar, Am, Aw and BS, BW, E, Ft and Fi. The Antarctic will lose 11.5% of the territory under Fi and Ft climates within the period between 2081 and 2100. The conclusion is that the climates BW, Bwh and Bwk, which represent the deserts, will increase by 119.8% with the increase of T by 4.3°C.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".