Spatially-resolved emulations of droughts and fire weather using MESMER-X
Bibliographic record
Abstract
Climate extremes are among the most impactful consequences of climate change. Moreover, droughts and fires may also hinder envisioned solutions to mitigate climate change, by reducing the efficiency of bio-energies with carbon capture storage and afforestation. Though, investigating such issues would benefit from a tool allowing fast computation of spatial climate extremes, for coupling to other models and exploration of scenarios. Here, we present an approach for emulating such extremes that are based on extensions of the spatially-resolved climate model emulator MESMER-X. In particular, we consider four annual indicators of the Canadian Fire Weather Index and the annual mean soil moisture derived from the Climate Model Intercomparison Project phase 6 for training and emulation. To emulate these indicators, we consider extensions to the framework that include the Gaussian and the Poisson distributions, non-linear evolutions of the parameters of the distribution and lagged effects. We show that the emulator reproduces the trajectories and the statistics of these annual indicators for fire weather and droughts accurately. By doing so, we show that the theoretical framework of MESMER-X can be applied for a large number of annual indicators of climate extremes.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".