Global sensitivity analysis of the climate–vegetation system to astronomical forcing: an emulator-based approach
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Bibliographic record
Abstract
Abstract. A global sensitivity analysis is performed to describe the effects of astronomical forcing on the climate–vegetation system simulated by the model of intermediate complexity LOVECLIM in interglacial conditions. The methodology relies on the estimation of sensitivity measures, using a Gaussian process emulator as a fast surrogate of the climate model, calibrated on a set of well-chosen experiments. The outputs considered are the annual mean temperature and precipitation and the growing degree days (GDD). The experiments were run on two distinct land surface schemes to estimate the importance of vegetation feedbacks on climate variance. This analysis provides a spatial description of the variance due to the factors and their combinations, in the form of "fingerprints" obtained from the covariance indices. The results are broadly consistent with the current under-standing of Earth's climate response to the astronomical forcing. In particular, precession and obliquity are found to contribute in LOVECLIM equally to GDD in the Northern Hemisphere, and the effect of obliquity on the response of Southern Hemisphere temperature dominates precession effects. Precession dominates precipitation changes in subtropical areas. Compared to standard approaches based on a small number of simulations, the methodology presented here allows us to identify more systematically regions susceptible to experiencing rapid climate change in response to the smooth astronomical forcing change. In particular, we find that using interactive vegetation significantly enhances the expected rates of climate change, specifically in the Sahel (up to 50% precipitation change in 1000 years) and in the Canadian Arctic region (up to 3° in 1000 years). None of the tested astronomical configurations were found to induce multiple steady states, but, at low obliquity, we observed the development of an oscillatory pattern that has already been reported in LOVECLIM. Although the mathematics of the analysis are fairly straightforward, the emulation approach still requires considerable care in its implementation. We discuss the effect of the choice of length scales and the type of emulator, and estimate uncertainties associated with specific computational aspects, to conclude that the principal component emulator is a good option for this kind of application.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it