Stevenson (2026) Single model initial-condition large ensembles

Identification

• Journal: Elsevier eBooks
• Year: 2026
• Date: 2026-01-01
• Authors: Samantha Stevenson
• DOI: 10.1016/b978-0-443-15748-6.00028-9

Research Groups

University of California, Santa Barbara, Santa Barbara, CA, United States

Short Summary

This paper reviews Single-Model Initial-Condition Large Ensembles (SMILEs), highlighting their utility in distinguishing forced climate responses from internal variability and their application in quantifying rare events and assessing future climate change. It discusses their methodology, applications, and current challenges, including inter-model disagreements and biases.

Objective

• To review the characteristics, methodologies, and applications of Single-Model Initial-Condition Large Ensembles (SMILEs).
• To discuss the utility of SMILEs in separating forced climate responses from internal variability and quantifying rare events.
• To identify current challenges and areas of disagreement in SMILEs, such as inter-model differences in coupled climate modes and persistent model biases.

Study Configuration

• Spatial Scale: Global to regional scales (e.g., global oceans, arid regions, small spatial scales).
• Temporal Scale: Decadal to multi-decadal and longer timescales (e.g., beyond 10–15 years, short timescales, long-term trends, observational period).

Methodology and Data

• Models used: Earth system models (ESMs), specifically those from Coupled Model Intercomparison Project phases 5 and 6 (CMIP5/6) model generations.
• Data sources: Single-Model Initial-Condition Large Ensembles (SMILEs) simulations.

Main Results

• SMILEs provide the best available estimates of internally generated climate variability.
• SMILEs are available with a wide variety of models, external forcing scenarios, and initialization strategies.
• Initialization strategy does not exhibit a large influence on most climate variables beyond 10–15 years after the simulation start.
• In SMILE projections, the forced response in many climate variables emerges from internal variability over the observational period, with the time of emergence dependent on the quantity considered.
• Substantial inter-model disagreement exists in climate variability and the patterns of long-term trends, suggesting inter-model differences in coupled climate modes are a significant issue.
• SMILEs are valuable resources for quantifying rare events, which is difficult to achieve in observations or individual model simulations.

Contributions

• Provides a comprehensive review and synthesis of the current state of SMILEs, their applications, and limitations.
• Emphasizes the critical role of SMILEs in disentangling forced climate change signals from internal variability.
• Highlights the unprecedented opportunity SMILEs offer for statistically robust characterization of climate change and internal variability.
• Identifies key challenges, such as inter-model disagreement in climate variability and persistent model biases, guiding future research directions.

Funding

Not specified in the provided text.

Citation

@article{Stevenson2026Single,
  author = {Stevenson, Samantha},
  title = {Single model initial-condition large ensembles},
  journal = {Elsevier eBooks},
  year = {2026},
  doi = {10.1016/b978-0-443-15748-6.00028-9},
  url = {https://doi.org/10.1016/b978-0-443-15748-6.00028-9}
}