Mapped climate metrics
Temperature, water balance, agriculture, and ecosystem indicators are available as consistent spatial layers.
Product roadmap
From climate metrics to state-of-climate intelligence.
MetriClima already makes climate evidence explorable. The road ahead is to connect those observations into a defensible assessment of whether a place has entered a new climate state, why it matters, and what remains reliable.
Available today
A climate metrics explorer with a rigorous data foundation.
The current product is designed to expose climate evidence clearly and consistently across locations, variables, and reference periods.
Spatial exploration
Users can move from broad regional patterns to country and location-level climate context.
Climate-period comparison
Historical normals and changes between reference periods make long-term movement visible rather than hiding it in a single average.
Reproducible methodology
Earth observation and climate reanalysis inputs are processed through documented, repeatable analytical workflows.
What it answers
What does the evidence show?
- What is the climate of this location?
- How has a metric changed between periods?
- Where are patterns strongest?
- Which variables shape local suitability?
The interpretation gap
Separate observations do not yet describe the state of a climate.
A place can retain similar annual rainfall while becoming drier in spring, wetter in autumn, more dependent on intense events, and exposed to longer droughts.
Metrics explorer
- Spring rainfall decreased
- Autumn rainfall increased
- Heavy rainfall intensified
- Dry periods lengthened
- Growing season shifted
Five valid observations.
State-of-climate intelligence
One coherent, evidence-linked interpretation.
The goal is not to replace climatology, but to integrate its findings into scientifically defensible climate knowledge.
The road ahead
Four stages from measurement to intelligence.
These stages describe dependency and scientific maturity, not fixed delivery dates. Each layer must remain traceable to the evidence beneath it.
- 01Available now
Climate evidence foundation
Continue strengthening coverage, location detail, comparison tools, and transparent methodology.
- Broader indicator coverage
- Consistent location summaries
- Clearer provenance
- Stronger comparison workflows
- 02Next
Interpretation layer
Organize related metrics into interpretable signal families without collapsing them into a premature black-box score.
- Trend and rate of change
- Seasonal redistribution
- Variability and persistence
- Extremes and recurrence
- 03Research and validation
Climate-state assessment
Develop open methods to detect and characterize possible climate-state transitions, evaluate climate reliability and place observed changes in the context of natural variability, Earth-system memory and the evolving background climate.
- Detecting and characterizing possible climate-state transitions
- Evaluating climate reliability
- Driver-aware interpretation
- Communicating confidence, provenance and limitations
Interpretive rather than predictive; causal attribution would require an appropriate validated methodology.
- 04Longer-term direction
Decision-ready climate knowledge
Serve the same scientific core as a clear human explanation and as structured, machine-readable context for trustworthy decision support.
- Location-level narratives
- Decision context by use case
- Machine-readable semantics
- Evidence-linked AI outputs
Scientific frontier
Compare evolving climate states, not periods in isolation.
Two years with a similar ENSO phase can still produce different regional outcomes because the Earth-system background state has changed.
Human forcing
GHGs · aerosols · land-use change
Earth-system memory
Ocean heat · cryosphere · soils · vegetation
Natural variability
ENSO · NAO · AO · PDO · AMV
Local geography
Topography · coastlines · land cover · regional circulation
Observed regional climate state
Regional expression · reliability · confidence
Product principles
Intelligence must remain accountable to evidence.
Open before composite
Indicator selection, assumptions, and weighting must be visible and scientifically defensible.
Uncertainty is a result
Confidence and limitations belong in the assessment, not in a hidden disclaimer.
Local context matters
Regional geography and climate behavior must remain visible beneath every summary.
One core, two audiences
Human explanations and AI-readable outputs must share the same evidence and provenance.
A roadmap for collaboration
The transition requires product learning and scientific partnership.
Regional pilots, methodological research, and user feedback will determine which interpretations are useful, robust, and ready to become product capabilities.