PCA-OS: A Planetary Climate Adaptation Operating System

Abstract

Predictive climate machine learning is increasingly good at forecasting hazards, but hazard maps alone do not decide what to do, where, when, for whom, and under which futures. We argue that climate ML remains insufficient for adaptation unless interventions become first-class, versioned, and auditable objects. Many climate digital twins still prioritize state estimation and simulation, whereas adaptation requires intervention observability, counterfactual effect estimation, and constrained portfolio choice. We propose PCA-OS (Planetary Climate Adaptation Operating System), a decision-support operating abstraction built on an intervention-aware global causal knowledge graph. PCA-OS standardizes schemas, versioned updates, query primitives, and audit interfaces across three core objects: (1) an Adaptation Intervention Ledger recording measurable interventions with provenance and uncertainty; (2) a Causal Effect Atlas storing scenario-indexed, spillover-aware estimands, identification assumptions, diagnostics, and sensitivity bounds; and (3) a Robust Portfolio Decision Layer optimizing intervention portfolios under budget, equity, and no-harm constraints. Foundation models and intervention-aware world models should support, not replace, identification-aware causal analysis by surfacing candidate confounders, mechanisms, and spillover pathways for human review. We also outline AdaptBench, an evaluation suite where systems can fail for inequitable or maladaptive recommendations despite high predictive accuracy. The result is a field-level provocation: move climate ML from read-only hazard intelligence to auditable decision support for adaptation.