AI-Assisted Seasonal and Decadal Climate Prediction

Research & Development · Ecology · Planning & Decision-making

What it collects

Operational data

Anonymized data

Seasonal and decadal climate predictions generated by the Canadian Centre for Climate Modelling and Analysis Earth System model (CRD's Earth System model). These are geophysical model outputs with no personal information.

Government of Canada AI Register — 2526-ECCC-011

Run by: Environment and Climate Change Canada (ECCC)
Where: No fixed location
Kept: Not stated by the Helpful Places.
Shared with: Accountable organization
Your copy: You cannot see the data it holds about you. What you can do

What it is for

Research & Development

Ecology

Planning & Decision-making

This system uses artificial intelligence and machine learning to improve the accuracy of seasonal-to-decadal climate forecasts produced by Environment and Climate Change Canada. It applies neural networks and generative AI models to post-process outputs from an Earth System model, with the aim of sharpening forecast performance and better capturing extreme weather events. The system is currently in development and is used by Government of Canada employees, not members of the public. It does not involve personal information.

What it collects and what happens to it

Data taken in

Operational data

Anonymized data

Seasonal and decadal climate predictions generated by the Canadian Centre for Climate Modelling and Analysis Earth System model (CRD's Earth System model). These are geophysical model outputs with no personal information.

Government of Canada AI Register — 2526-ECCC-011

Processing

Classification & Prediction

Artificial Neural Networks are applied to post-process Earth System model outputs, producing improved deterministic climate predictions and forecasts covering seasonal to decadal timescales.

Government of Canada AI Register — 2526-ECCC-011

What it does

Deciding (Analytical AI)

Human decides

Artificial Neural Networks (ANNs) are used deterministically to classify, predict, and post-process climate model outputs — producing improved forecast scores and ranked predictions of climate states. Human scientists review and use these outputs.

Government of Canada AI Register — 2526-ECCC-011

Creating (Generative AI)

Human decides

Conditional Variational Autoencoders (CVAEs), a generative AI architecture, are used to sample and generate plausible climate scenarios — particularly for better representing extreme events in forecast ensembles.

Government of Canada AI Register — 2526-ECCC-011

Outputs

About a measurement

Anonymized data

Improved seasonal-to-decadal climate forecast outputs, including post-processed prediction values and generated samples of extreme climate events, for use by Government of Canada scientists and decision-makers.

Government of Canada AI Register — 2526-ECCC-011

Generated content

Anonymized data

Synthetic climate scenarios generated by Conditional Variational Autoencoders to better sample the distribution of extreme climate events beyond the range of the original Earth System model ensemble.

Government of Canada AI Register — 2526-ECCC-011

Run by

Environment and Climate Change Canada (ECCC)

The Canadian Centre for Climate Modelling and Analysis (CCCma), a branch of Environment and Climate Change Canada, develops and operates this AI system for internal research and forecast improvement purposes.

Government of Canada AI Register — 2526-ECCC-011

Built by

Not stated by the Helpful Places.

Kept for

Not stated by the Helpful Places.

Shared with

Not available to me

This system is used exclusively by Government of Canada employees (primarily researchers at CCCma). Output data is not made available to the general public through this system.

Government of Canada AI Register — 2526-ECCC-011

Available to the accountable organization

Forecast outputs are available to Environment and Climate Change Canada employees, specifically GC employees identified as the primary users.

Government of Canada AI Register — 2526-ECCC-011

Stored

Not stated by the Helpful Places.

How to read the colours

Can it identify you?

Anonymized data: Data about people with the link to who is broken. Stripped of identifiers, blurred, aggregated, or noised so this system can’t reasonably tie a record back to an individual.
Pseudonymous data: Each person’s data is tied to a token (hash, ID, template) that lets this system recognise the same person across events, but the token itself doesn’t reveal a name. Reidentification is possible with extra information.
Identifiable data: The data either contains a direct identifier (name, address, account name, recognisable face or voice, plate number) or carries a token this system uses to look up legal identity during processing.

Who completes the loop?

Human decides: This mode suggests; a person decides what to do next. The AI is always advisory — a human is in the loop on every decision. Example: a triage tool ranks cases for a clinician who chooses which to see first.
Human executes: This mode decides; a person carries out the result. Example: an optimizer plans the day’s trash-collection routes, and drivers run them.
Autonomous: This mode decides and acts on its own. No person reviews each decision or carries out the resulting action.

Definitions from the DTPR standard. Amber is about your data, violet about who decides. The fuller the shape and the deeper the colour, the more identifying the data or the less a person is involved.

What you can do

Ask about this system

Questions go to the Helpful Places, not the vendor.

Your rights

The Helpful Places has not listed specific rights for this system.

Risks and safeguards

Societal & cultural harmAI-generated climate forecasts that diverge significantly from physical reality could misinform government policy decisions on climate adaptation and emergency preparedness.Safeguard: The system is in development, used internally by expert scientists who can evaluate outputs critically; open-source tools and peer-review norms of the scientific community provide additional quality controls.