Global Monthly Temperature Ensemble with 200 Members and Infilled Coverage

DCENT-I is a globally infilled, spatially complete monthly temperature dataset at 5°×5° resolution from 1850 to the present. It consists of 200 ensemble members that sample uncertainties from bias adjustments and the infilling process. The dataset was produced by Duo Chan via Harvard Dataverse and was updated with data through the end of 2025.

Use Cases

• Analyze century-scale warming patterns like stronger continental versus ocean warming using the 5°×5° gridded temperature fields.
• Quantify uncertainty in global mean surface temperature (GMST) estimates by sampling across the 200 ensemble members.
• Study early El Niño event reconstruction, such as the 1887 event, using the infilled, globally complete temperature anomalies.
• Compare infilling impact by assessing the reported 0.08 °C increase in GMST warming estimate relative to the original DCENT ensemble.

Strengths

• Provides 200 ensemble members for quantifying uncertainty.
• Offers spatially complete global coverage from 1850 to present via infilling.
• Data is updated through the end of 2025, ensuring recent coverage.

Limitations

• Spatial resolution is 5°×5°, which is coarse for regional or local-scale analysis.
• Infilling process introduces its own uncertainty, quantified as a 0.08 °C GMST adjustment.
• Specific sample size (row count) and file sizes are not provided in the description.

Provenance

Source

Harvard Dataverse, authored by Duo Chan.

Collection Method

Produced by infilling land-air and sea-surface temperatures from the DCENT dataset using ordinary kriging with anisotropic and heterogeneous kernels, incorporating air-temperature anomalies over sea-ice areas.

Time Range

1850 to present (through 2025).

Freshness

Updated through the end of 2025, with repository update in March 2026.

Geography

Global coverage at 5°×5° resolution.