Berner et al. (2026) Quantifying Sources of Subseasonal Prediction Skill in CESM2 Within a Perfect Modeling Framework

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Geophysical Research Letters
• Year: 2026
• Date: 2026-04-03
• Authors: Judith Berner, Abigail Jaye, Jadwiga H. Richter, Anne Alexandra Glanville
• DOI: 10.1029/2025gl120435

Research Groups

Not specified in abstract.

Short Summary

This study uses a perfect modeling framework with the Community Earth System Model to estimate the theoretical limit of subseasonal-to-seasonal predictability from initialization, revealing that land initialization is the dominant source of predictability over land beyond week four.

Objective

• To estimate the theoretical limit of subseasonal-to-seasonal predictability from initialization using a perfect modeling framework.

Study Configuration

• Spatial Scale: Global (implied by Earth System Model)
• Temporal Scale: Subseasonal-to-seasonal (beyond week four)

Methodology and Data

• Models used: Community Earth System Model (CESM), perfect modeling framework
• Data sources: Model-generated data within a perfect modeling framework

Main Results

• Over land, land initialization is the dominant source of predictability beyond week four.
• Ocean initialization plays a secondary role in contributing to predictability over land beyond week four.

Contributions

• Provides an estimate of the theoretical upper limit of subseasonal-to-seasonal predictability from initialization.
• Highlights the substantial potential to advance prediction through improved land initialization and enhanced representation of land-atmosphere coupling.

Funding

Not specified in abstract.

Citation

@article{Berner2026Quantifying,
  author = {Berner, Judith and Jaye, Abigail and Richter, Jadwiga H. and Glanville, Anne Alexandra},
  title = {Quantifying Sources of Subseasonal Prediction Skill in CESM2 Within a Perfect Modeling Framework},
  journal = {Geophysical Research Letters},
  year = {2026},
  doi = {10.1029/2025gl120435},
  url = {https://doi.org/10.1029/2025gl120435}
}