xai-cons

What problem does it solve?

This Skill specializes in Observational Constraint (Emergent Constraint, EC) analysis - a method that uses historical observations to constrain future climate projections across CMIP multi-model ensembles, reducing prediction uncertainty. It includes inter-model regression analysis, EC relationship establishment, physical mechanism diagnostics (residual analysis, teleconnection pathways, Walker circulation, lead-lag correlation, and SVD), uncertainty quantification (variance reduction, and confidence intervals), and reliability assessment (binning analysis, random EC comparison).

Core Features & Use Cases

• EC Relationship Establishment: Load CMIP6 multi-model historical and future data, compute regional averages, perform inter-model linear regression, and output β, R², r, p-value, and constrained predictions.
• Reliability Assessment: Apply binning analysis and random EC comparisons to assess reliability; produces prior/posterior distributions and percent variance reduction.
• Physical Mechanism Diagnostics: Residual analysis, mediation analysis, teleconnections (e.g., Walker circulation) to validate mechanisms behind ECs.

Quick Start

Use the EC workflow to constrain a CMIP6 variable pair (e.g., South Atlantic SST vs East Asia TAS) by applying historical observations to the emergent relationship and obtaining a constrained future projection.