Online particle-filter OSSE diagnostics and experiment code for AMOC reconstruction (pypfda v1.0)

Data and analysis/experiment code underlying Fallah et al., 'pypfda v1.0: an open-source, model-agnostic particle-filter engine for online sequential paleo data assimilation in Earth system models' (submitted to Geoscientific Model Development, 2026). Contains the perfect-model OSSE diagnostics (truth, FREE and DA ensemble AMOC and subpolar-gyre SST series for the 100-year campaign and the 300-year run; effective-sample-size, importance-weight and full genealogy histories; the 59-site pseudo-observation network), the CLIMBER-X second-core reconstruction, the eta x ESS sensitivity sweep, and the scripts that regenerate every figure and table, including the CM2Mc-BLING SLURM orchestrator (run_online_da.py) and the cost/weight module. The assimilation engine itself is the separate pypfda software record (doi:10.5281/zenodo.21281526). Raw coupled-model output (~16 TB restarts and the 3.25 GB CLIMBER-X truth history) is impractical to archive publicly and is available from the authors on request; see AVAILABILITY.md. [v1.1, 2026-07-10] Added the fair online-vs-offline AMOC benchmark: the exact noisy 59-site pseudo-observations assimilated by the 300-yr run, and scripts reproducing (A) parity between the particle filter and the best linear map from those observations at the assimilation times, and (B) online DA exceeding an offline linear reconstruction interpolated to annual resolution between the sparse observations; plus the nonlinear-observation-operator Lorenz-63 demonstration (particle filter r=0.82 vs linear regression 0.40 under a saturating operator). [v1.2, 2026-07-10] Added the two-panel online-vs-offline figure and the refined analysis: online DA reconstructs the between-observation AMOC variability the sparse 5-yr proxies cannot resolve (ensemble-mean r=0.53 vs 0.28 for an offline linear reconstruction; a perfect-model upper bound), plus the 100-member skill spread; scripts/online_vs_offline.py regenerates the figure and all numbers.