Multi-decadal trends of low clouds at the Tropical Montane Cloud Forests
Bibliographic record
Abstract
Clouds are critical to the biodiversity and function of Tropical Montane Cloud Forests (TMCF). These ecosystems provide vital services to humanity and are considered hotspots of endemism, given that the number of species is restricted to their microclimates. Cloudiness (e.g., the fraction of low-clouds) in these ecosystems is projected to decline owing to global warming, but recent temporal trends remain unclear. Here, we evaluated trends in low-cloud fractions (CF) and other Essential Climatic Variables (ECV) (e.g., surface temperature, pressure, soil moisture, and precipitation) for 521 sites worldwide with TMFCs from 1997 to 2020. Thus, we hypothesize that recent traces of global warming over the last few decades have led to decreases in low-cloud cover on TMCFs. The previous study was also evaluated globally and among biogeographic realms to identify regional trends. We computed trends by aggregating hourly observations from ERA5 reanalysis and CHIRPS into annual averages and then used linear regressions to calculate slopes (i.e., rate of change) (Δ, year -1 ). Our results suggest that CF trends at the TMCFs range between -64.7 ×10 -4 and 51.4 ×10 -4 CF year -1 , revealing that 70% of the assessed sites have experienced reductions in CF. Declines in low-clouds in these ecosystems are 253% more severe than tropical landmasses when peak values of density distribution are compared (TMCFs: -7.8 ×10 -4 CF year -1 ; tropical landmasses -2.3 ×10 -4 CF year -1 ). Despite this, CF trends tend to differ among biogeographic realms, as those TMCFs from the Neotropics and Indomalayan realms have the most pronounced declines. Decreases in CF were also associated with increases in surface temperature and pressure and decreases in soil moisture, revealing that the TMCF’s climate is changing to warmer environments. These climatic shifts may represent a fingerprint of global change on TMCFs, highlighting a current threat to species and essential ecosystem services that these ecosystems provide.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".