dataset · Global
Community Earth System Model Large Ensemble (CESM LENS)
The Community Earth System Model (CESM) Large Ensemble Numerical Simulation (LENS) dataset includes a 40-member ensemble of climate simulations for the period 1920-2100 using historical data (1920-2005) or assuming the RCP8.5 greenhouse gas concentration scenario (2006-2100), as well as longer control runs based on pre-industrial conditions. The data comprise both surface (2D) and volumetric (3D) variables in the atmosphere, ocean, land, and ice domains. The total data volume of the original dat
Visit source ↗Source details
Licence: https://www.ucar.edu/terms-of-use/data
Evidence quality: 40.1
Listed in aws-odr public catalogue API on 2026-10-16; relevance/value screened (haiku).
Metadata checked: 2026-10-14T13:20:00.000Z
What's in it
- Data offered docs-verified
- 40-member climate ensemble simulations (1920-2100) · 2D and 3D atmosphere, ocean, land and ice variables · pre-industrial control runs
- Coverage llm-inferred
- Global · 1920-2100
- Resolution docs-verified
- Temporal: monthly, daily, 6-hourly; spatial resolution not stated
- Update frequency docs-verified
- Rare (experiment complete; occasional additional fields or fixes)
- Formats docs-verified
- Zarr
- Access docs-verified
- Public anonymous S3 access, no AWS account required (us-west-2); licence: https://www.ucar.edu/terms-of-use/data
Useful for
- Use cases llm-inferred
- Ensemble-based climate change context for drainage or flood risk sensitivity studies · Internal-variability envelope for climate projections in FRA or water resources assessments · Temperature and precipitation trend baselines for environmental impact assessments
- Audience llm-inferred
- hydrologists · flood risk consultants · drainage engineers · climate scientists · GIS analysts
Limitations
- Constraints llm-inferred
- Only a subset (~70 TB) of the ~500 TB original dataset is on AWS · Scenario coverage limited to historical plus RCP8.5 (2006-2100) · Global model output, not UK-specific · Updates are rare; experiment is complete · Requires Zarr and Python Xarray tooling · Large volumes; analysis needs cloud or high-capacity compute
Evidence
Check upstream coverage, uncertainty and licence before use. Station observations are not forecasts or design levels.