On the Relationship Between Probabilistic and Deterministic Skills in Dynamical Seasonal Climate Prediction
Bibliographic record
Abstract
Abstract Despite the gradually increasing emphasis on assessing the skill of dynamical seasonal climate predictions from the probabilistic perspective, there is a lack of in‐depth understanding that an inherent relationship may exist between the probabilistic and deterministic seasonal forecast skills. In this study, we focus on investigating this relationship, through theoretical consideration based on an analytical approach and diagnostic analysis of the historical forecasts produced by multiple dynamical models. The probabilistic forecast skill is gauged in terms of its two different attributes: resolution and reliability, while the deterministic forecast skill is measured in terms of anomaly correlation (AC). Through the theoretical consideration under certain simplified assumptions, a nonlinear, monotonic relationship is analytically derived between the resolution and the AC. Subsequent diagnostic analysis shows that the resolution and AC skills of both the multimodel ensemble and its member single models indeed appear to be approximately monotonically and nonlinearly related, specifically when they are calculated in a zonally aggregated manner by which the impact of finite sample size is reduced. This observed relationship has a specific form that is consistent with what the theory predicts. In short, the theoretical result is well verified by the dynamical model forecasts. Diagnostic analysis also shows that no good relationship exists between the reliability and the AC, signifying the difference of reliability and resolution in nature. A specific application of the proven resolution‐AC coherence is also demonstrated. The proved resolution‐AC relationship can facilitate comparisons among various assessments of seasonal climate prediction skill from the deterministic or probabilistic perspective alone.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".