Causality in long-term predictions, past-value problems and a stochastic-deterministic hybrid
Bibliographic record
Abstract
Over time scales between 10 days and 10-20 years – the macroweather regime – atmospheric fields, including the temperature, respect statistical scale symmetries, such as power-law correlations, that imply the existence of a huge memory in the system that can be exploited for long-term forecasts. The Stochastic Seasonal to Interannual Prediction System (StocSIPS) is a stochastic model that exploits these symmetries to perform long-term forecasts. It models the temperature as the high-frequency limit of the fractional energy balance equation (fractional Gaussian noise) which governs radiative equilibrium processes when the relevant equilibrium relaxation processes are power law, rather than exponential. The multivariate version of the model (m-StocSIPS), exploits the space-time statistics of the temperature field to produce realistic global simulations, including realistic teleconnection networks and El Niño events and indices. One of the implications of this model is the lack of Granger-causality: the optimal predictor at gridpoint i is obtained from the past of the timeseries i and cannot be improved using past temperatures from any other location j. This allows to treat predictions for long-memory processes as “past value” problems rather than the conventional initial value approach that uses the current state of the atmosphere to produce ensemble forecasts. To improve the stochastic predictions, a zero-lag independent (non-stochastic) predictor is needed. Here we use the Canadian Seasonal to Interannual prediction System (CanSIPS), as a deterministic co-predictor. CanSIPS is a long-term multi-model ensemble (MME) system using two climate models developed by the Canadian Centre for Climate Modelling and Analysis (CCCma). The optimal linear combination of CanSIPS and StocSIPS (CanStoc) was based on minimizing the square error of the final predictor in the common hindcast period 1981-2010 using different out-of-sample validations. Global time series and regional maps at 2.5ºx2.5º resolution show that the skill of CanStoc is better than that of each individual model for most of the regions when non-overlapping training and verification periods are used.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".