Open run data

The receipts point here.

Every number published on this site links back to a raw pipeline output. These are those files — unedited JSON, exactly as the Arasense engine wrote them. Download them, interrogate them, recompute them.

bologna-rx1day-deep-dive.json

The full 34-model Bologna projection: every model's trust weight, both rejections, the spread, and the agreement behind the +14.2% headline.

RAW JSON →
bologna-aras-diagram.json

The Aras Diagram coordinates of the Bologna trust screening: every model's bias, variability, and correlation error, its weight and tier — including one borderline model flagged because it drifted above the rejection floor between runs.

RAW JSON →
bologna-ssp245-vs-ssp585.json

SSP2-4.5 vs SSP5-8.5 for Bologna, sharing one observed baseline and one set of trust weights — only the emissions scenario changes.

RAW JSON →
italian-flood-portfolio.json

The five-city Italian portfolio ranking: Rome, Milan, Florence, Bologna, Venice — with the agreement column that changes the story.

RAW JSON →
european-trust-atlas.json

The European Trust Atlas screening runs behind the ranked map — one method, one configuration, every city.

RAW JSON →
world-trust-atlas.json

The global preview runs, including the full Mexico City arc: the implausible number caught at launch, and the honest signal published after the extreme-plausibility screen was built because of it.

RAW JSON →
mexico-city-full-projection.json

The full-ensemble Mexico City investigation run: 31 of 34 models trusted on mean climate, several carrying physically impossible daily extremes — the evidence that mean-climate skill does not certify extreme-value sanity.

RAW JSON →
atlas-reverification-2026-07-05.json

The re-verification sweep of all 36 published atlas numbers under the extreme-plausibility screen: 35 matched within 0.02 points, the largest deviation 0.1 points.

RAW JSON →
drift-ledger.json

The Screening Drift Ledger itself — the append-only record behind the Drift Ledger page: every re-run of a published screening, what moved, and what was done about it.

RAW JSON →
How to read these files. All projections share one method: models scored against the observed ERA5-Land climatology with the Aras Diagram (Izzaddin et al. 2024), trust-weighted, then projected for 2040–2059. Atlas files use a 5-model screening ensemble; the Bologna deep dive screens all 34 CMIP6 models. Screening-level outputs are for prioritization, not site engineering. If you use these files, cite the method paper and link back here.