Climate & Environment
Climate Science
Discover energy balance laws, circulation equations, and feedback mechanisms from climate observations and reanalysis data.
One of 34 industries across 8 sectors served by CDE — the Causal Dynamics Engine for Science and Engineering.
The Challenge
Why Climate Science teams still struggle to explain what is happening
Climate science operates on observational records, satellite measurements, reanalysis products, and model outputs that together encode the governing dynamics of Earth's climate system: energy balance, atmospheric and oceanic circulation, radiative forcing, and feedback mechanisms. These datasets are massive, multivariate, and span decades. Yet extracting governing equations remains dominated by hypothesis-driven approaches where researchers propose specific model structures, parameterize sub-grid processes, and calibrate against observations, leaving large portions of the observational record underutilized for direct law discovery.
General circulation models encode decades of climate physics knowledge but rely on parameterizations for sub-grid processes (cloud formation, convective transport, boundary layer dynamics) that introduce systematic uncertainties. Statistical downscaling captures correlations but lacks the causal structure needed to distinguish forced responses from internal variability. Attribution studies require careful causal reasoning that purely correlative methods cannot provide. The result is a gap between available observational data and the governing equations that would enable more precise projections, especially for regional dynamics.
The CDE Approach
How CDE closes the explanation gap in climate science
CDE takes raw climate data (temperature records, precipitation time-series, radiative flux measurements, ocean heat content profiles) and discovers governing climate dynamics. Rather than requiring pre-specified model structures, it discovers energy balance laws, circulation equations, and feedback mechanisms directly from observational and reanalysis data, identifying the mathematical forms that best explain observed behavior without constraining the search to conventional parameterization families.
The Evidence Ledger provides deterministic replay for every climate discovery, ensuring governing equations can be independently verified, critical when policy decisions depend on scientific confidence. Conservation law detection validates energy and mass conservation, identifying violations that signal data quality issues or missing physical processes. The Truth Dial lets teams set confidence thresholds from exploratory research to policy-informing assessments.
Discovery Engine
How CDE applies here
Causal mode distinguishes forced responses from internal variability, maps teleconnection pathways, and quantifies feedback strengths, producing causal graphs that support attribution and projection. Symbolic mode discovers closed-form energy balance equations and circulation scaling laws from observational data. Neuro-Symbolic mode handles high-dimensional spatiotemporal structure, with the engine's internal methods capturing spatial patterns and structure extraction yielding interpretable governing laws for comparison with established climate physics.
Causal Graphs
Discovers directed causal structure from observational data — identifiable causal graphs, regime classifications, and intervention predictions.
Governing Equations
Extracts compact governing laws grounded in the causal structure — interpretable equations your team can read, verify, and compare against known theory.
Intervention Design
Proposes targeted experiments to resolve ambiguous causal edges — maximizing information gain where the causal structure is still uncertain.
Causal Validation
Negative controls, falsification tests, and identifiability analysis applied to every causal claim before promotion to the evidence ledger.
Typed Scientific Claims
What CDE discovers
Every discovery CDE produces is a typed scientific claim — not a black-box prediction, but a governed, reproducible, auditable piece of scientific knowledge with full provenance.
- Energy balance governing equations
- Circulation dynamics models
- Climate feedback mechanisms
- Teleconnection causal graphs
- Carbon cycle dynamics
Governed Discovery
Make the finding reviewable
Every discovery CDE produces carries the review context around it: a Truth Dial setting, an evidence entry with replay context, and control results including bootstrap stability, out-of-distribution testing, and feature-shuffle validation.
For climate science, that means teams can compare runs, justify decisions, and decide whether a finding is ready for internal use, external review, or regulated submission.
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Put CDE on a real climate science problem
Whether you are exploring climate science data for the first time or scaling an existing research programme, CDE adapts to your workflow. Bring the dataset, the decision pressure, and the constraints. We will map the right discovery path.