Assessing future projections of warm-season marine heatwave characteristics with CMIP6 models
Bibliographic record
Abstract
Marine heatwaves in the summertime when temperatures may exceed organisms’ thermal thresholds (“warm-season MHWs”) have huge impacts on the health and function of ecosystems like kelp forests and coral reefs. While previous studies showed that MHWs are likely to become more frequent and severe under future climate change, there has been less analysis of the thermal properties of warm-season MHWs or on the effects of climate model biases on these projections. In this study, we examine CMIP6 model ability to simulate five key thermal properties of warm-season MHWs, and evaluate the global pattern of future projections for coral reef and kelp systems. The results show that the duration, accumulated heat stress and peak intensity are projected to increase by > 60 day, 160 °C·day and 1 °C, respectively, across most of the ocean by the end of 21st century. In contrast, the duration of “priming” (a period of sub-lethal heat stress prior to MHW development) is projected to decrease by > 30 day in the tropics, potentially reducing organisms’ ability to acclimate to heat stress. The projected increases in the MHW duration and accumulated heat stress in some coral reef and kelp forest locations, however, are likely overestimated due to model limitations in simulating surface winds, deep convections and some other processes that influence MHW evolution. The findings point to the processes to target in model development and regional biases to be considered when projecting the impacts of MHWs on marine ecosystems.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".