Coupled Model Intercomparison Project Phase 6 - CMIP6

Summary Description

The CMIP6 (Coupled Model Intercomparison Project Phase 6) is the latest phase of the international CMIP effort, coordinated by the World Climate Research Programme (WCRP). It provides global climate simulations from multiple modeling centers and is a cornerstone dataset for IPCC AR6.
CMIP6 includes historical simulations, future scenarios (SSPs), and targeted experiments supporting studies on climate variability, extremes, and impacts.

CMIP6 is a comprehensive collection of multi-model climate simulations that cover historical and scenario-based projections.
It provides temperature, precipitation, pressure, wind, and other climate variables essential for climate impact studies, including electricity sector planning.

CMIP6 Global Projections

Global CMIP6 Example Output (source: Eyring et al., 2016)

Dataset Characteristics

  • Current version: CMIP6 (2019–present)
  • Temporal coverage: 1850–2100+
  • Temporal resolution: Daily, monthly, and annual
  • Spatial coverage: Global (land + ocean)
  • Spatial resolution: ~1° to ~2° depending on model
  • Data type: Gridded NetCDF (multi-variable, multi-model)
  • Web references:
    CMIP6 on ESGF
  • Reference publications:
    See references below

Strengths and Limitations

Key Strengths of CMIP6

Strength Description
Global Coverage Provides climate projections for the entire globe, both land and ocean.
Multi-Model Ensemble Reduces uncertainty through ensemble approaches across many GCMs.
Scenario Diversity Includes multiple Shared Socioeconomic Pathways (SSPs) and targeted experiments.
Long-term Historical + Future Data Continuous coverage from 1850 to 2100+.

Key Limitations of CMIP6

Limitation Description
Coarse Resolution Grid spacing (~100 km) is insufficient for local infrastructure studies.
Biases in Climate Models Systematic errors in precipitation and extremes.
Data Size and Complexity Multi-model, multi-variable datasets require HPC or cloud resources to process.
No Direct Observational Data Requires downscaling and bias-correction for applied studies.

Expert Guidance

  • Use bias-corrected and downscaled CMIP6 data (e.g., CORDEX or statistically downscaled products) for grid-level planning.
  • Combine multiple GCMs to assess climate uncertainty for electricity system planning and infrastructure design.
  • Focus on relevant variables like temperature, precipitation, and wind for demand forecasting, hydropower analysis, and cooling water assessments.

Example Applications

Links to Electricity Sector Activities:

  • Electricity System Planning – Demand and generation forecasts under climate change
  • Operations Planning – Assess risks to hydropower operations from variability and extremes
  • Infrastructure Planning and Asset Management – Evaluate long-term exposure to extreme heat and drought for transmission lines and cooling water supply
  • Assurance and Reporting – Provide climate risk evidence for ESG and regulatory disclosures

Variables available in CMIP6

Click on variable groups to uncollapse

Daily, monthly, and annual mean near-surface temperature.

Total precipitation (rain + snow), often used for hydropower and water resource studies.

Useful for wind energy studies and storm impact analysis.

Supports extreme weather and storm track analysis.

Radiation, evaporation, runoff, and cloud cover available depending on the model.

Data Access

References