Climate Change Scenarios in the Southern Caribbean region of Central America
Bibliographic record
Abstract
Introduction: Warming is already significant in Central America and the Caribbean and may be magnified even further in the future. A decrease in the precipitation is projected, increasing also regional aridity. Objective: To study observed and projected latitudinal gradients for precipitation and temperature in three Southern Caribbean locations of Central America: Bluefields (Nicaragua), Limón (Costa Rica) and Bocas del Toro (Panamá) and to characterize their future changes and determine if there are differences or similarities in a north-south direction. Methods: Monthly precipitation (P) and temperature (T) data from General Circulation Models from 1979 to 2099, were downloaded from the WRF repository. Data from the selected models from the repository were subjected to a delta-type statistical downscaling to bring them to a resolution of 1 x 1 km. These models are part of the latest generation of the Coupled Model Intercomparison Project-Phase 6 used by the Intergovernmental Panel on Climate Change. The ground-truth data necessary for bias correction were obtained from the ERA5 reanalysis. Monthly P and T data were downloaded from 1979 to 2014 at different native spatial resolutions and climatologies at 1 x 1 km spatial resolution at global scales were obtained from WorldClim data. Results: Scenarios show that some regions would go from very humid to humid, based on strong reductions in precipitation and warming at the end of the 21st century. This expected increase in the aridity is going to have impacts on ecology and ecosystem services, agriculture, human consumption due to a water availability reduction per capita and hydroelectric generation. Conclusions: Generation of high spatial Climate Change scenarios is necessary because Central America is a region characterized by significant topographic complexity, land use variety and spatial occurrence of hydrometeorological disasters. This intrinsic variability suggests that local risk management and planning strategies must be designed with a highly specific approach to each locality or region. This implies that, even in areas geographically near to each other, the measures taken may not necessarily be transferable due to differences in climate projections, as it was found for the three nearby communities in the Southern Central American Caribbean coastal region.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".