Sources of uncertainty in estimation of climate velocity and their implications for ecological and conservation applications
Bibliographic record
Abstract
Abstract The velocity of climate change, which estimates the migration speed necessary to maintain constant climatic conditions, is increasingly used to map climate‐related threats to biodiversity. Using newly developed climate velocity data for North America to 2100 based on an ensemble of current‐generation climate projections, we asked how important differing sources of uncertainty from global climate model projections are, how the magnitude of this uncertainty compares with the internal variability of the climate system, and what aspects of climate velocity are robust to such uncertainty. We found that most variation was due to contrasts among global climate models, followed by variation among alternative emissions pathways. However, correlation was great enough (0.817) to allow application of velocity to inform conservation and management. In contrast, internal variability (i.e., weather at multidecadal timescales) resulted in low correlation between simulated and observed velocity for the 2001–2020 period. A null model using current baseline climate data and assumed uniform 2° heating was moderately correlated with velocity from ensemble future projections, helping to identify model‐independent velocity patterns difficult to capture via rules such as protection of elevational gradients. Such uncertainty analyses are essential for informed application of velocity and other climate exposure metrics.
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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 | 0.026 | 0.203 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".