How PaleoTech builds physics-driven climate intelligence.
PaleoTech’s climate-intelligence platform is built on a modular, first-principles framework that integrates Earth-system physics, signal analysis, orbital mechanics, hydrology, and climate-state inference.
Our technology reconstructs and predicts climate behaviour using interpretable, physics-driven signals rather than historical curve-fits or opaque machine-learning systems.
Climate-state inference allows PaleoTech to identify underlying regime shifts before they appear in traditional observations.
Each module contributes a different component of the climate system — rainfall, ENSO regime structure, axial tilt behaviour, seasonal patterning, mass redistribution, and long-lead climate signals. Together, they form an integrated engine for agriculture, climate services, and research.
Our system extends predictability windows by leveraging slow-moving physical drivers such as tilt anomalies, mass-flow redistribution, and ENSO regime transitions. — Driven by physics !
Technology Philosophy
PaleoTech’s approach is based on three principles:
1. Physics First
Climate behaviour is fundamentally driven by physical systems — not historical curve-fits.
Our framework captures Earth’s tilt, rotation, mass flow, redistribution, ocean–atmosphere coupling, and long-lead signals directly from first-principles dynamics.
2. Interpretable by Design
Every module exposes meaningful signal structure that can be traced, explained, and validated.
Signals from Earth-system dynamics convert directly into climate-state windows and decision-ready intelligence, producing clarity rather than black-box outputs.
3. Built for Real-World Application
Outputs support agriculture, climate services, environmental planning, hazards, and research organisations.
The system is engineered for operational use: transparent signals, stable forecasting, and actionable seasonal intelligence.
Why Physics Outperforms Traditional Climate Models
• Physical drivers change slowly → longer predictability
Tilt anomalies, momentum shifts, and mass redistribution evolve steadily, offering early warning.
• Physics does not require past patterns to repeat
Climate history is not stable — physics provides continuity even in non-stationary climate regimes.
• Physical signals reveal hidden structure
Regime shifts appear in underlying mechanics before surface variables respond.
• Physics enables interpretability
Every shift, anomaly, and forecast relates to a known mechanism — not a black box.
• Physics bridges climate and agriculture
Signals from Earth-system dynamics convert directly into seasonal windows and paddock decisions.
Tilt anomalies, momentum shifts, and mass redistribution evolve steadily, offering early warning.
• Physics does not require past patterns to repeat
Climate history is not stable — physics provides continuity even in non-stationary climate regimes.
• Physical signals reveal hidden structure
Regime shifts appear in underlying mechanics before surface variables respond.
• Physics enables interpretability
Every shift, anomaly, and forecast relates to a known mechanism — not a black box.
• Physics bridges climate and agriculture
Signals from Earth-system dynamics convert directly into seasonal windows and paddock decisions.