Future projections of temperature-related indices in Prince Edward Island using ensemble average of three CMIP6 models
Bibliographic record
Abstract
Prince Edward Island (PEI) is an agricultural province heavily relying on rainfed agriculture. The island has already experienced significant impacts from climate change. Accurate projections of PEI temperature extreme indices are required to mitigate and adapt to the changing climate conditions. This study aims to develop ensemble projections using Coupled Model Intercomparison Project Phase 6 (CMIP6) global circulation models (GCMs) to analyze temperature extremes on PEI. In this study, the ECMWF ERA5 reanalysis dataset was chosen for stepwise cluster analysis (SCA) due to its high accuracy. Three CMIP6 (NorESM2-MM, MPI-ESM1.2-HR, and CanESM5) GCMs, along with their ensemble average, were utilized in the SCA model to project future changes in daily maximum temperature (Tmax) and minimum temperature (Tmin) at four meteorological stations on PEI (East Point, Charlottetown, Summerside, and North Cape) under two shared socioeconomic pathways (SSP2-4.5 and SSP5-8.5). These GCMs were selected based on their low, medium, and high Equilibrium Climate Sensitivity. The bias-corrected results for the future period of Tmax and Tmin showed that the GCM-specific changes in the ECS also impact the regional scale. Additionally, several temperature extreme indices, including the daily temperature range (DTR), summer days (SU), growing degree days (GDD), growing season length (GSL), ice days (ID), and frost days (FD), were analyzed for two future periods: FP1(202-2050) and FP2 (2051-2075). The results indicate that DTR, SU, GDD, and GSL are expected to increase, while ID and FD are projected to decrease during FP1 and FP2 under both scenarios. The future projected mean monthly changes in Tmax, Tmin, and the selected temperature extreme indices highlight warmer future periods and an increase in agriculture-related indices such as GDD and GSL. Specifically, July, August, and September are expected to experience even higher temperatures in the future. As the climate becomes warmer, cold extreme events are projected to be shorter in duration but more intense in terms of their impact. The largest increments/decrements for Tmax, Tmin, and their relevant indices were observed during FP2 under SSP5-8.5. The outcomes of this study provide valuable insights for agricultural development, water resource management, and the formulation of effective mitigation strategies to address the impacts of climate change on PEI.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".