Irving et al. (2026) Quantifying the uncertainty associated with extreme weather likelihood estimates derived from large model ensembles

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

Identification

• Journal: Environmental Research Letters
• Year: 2026
• Date: 2026-07-03
• Authors: Damien Irving, Annette Stellema, Didier Paolo Monselesan, Carly R. Tozer, James S. Risbey
• DOI: 10.1088/1748-9326/ae860d

Research Groups

• Climate Science Centre, University of Melbourne
• Australian Bureau of Meteorology

Short Summary

This study investigates the uncertainties associated with estimating extreme weather event likelihoods using the UNprecedented Simulated Extremes using ENsembles (UNSEEN) approach on decadal hindcast data. The results suggest that multi-model analysis is essential for reducing uncertainty in UNSEEN-based estimates.

Objective

• Investigate the impact of bias correction and model diversity on the uncertainty of extreme event likelihoods estimated using the UNSEEN approach.

Study Configuration

• Spatial Scale: Australia-wide, with a focus on temperature and rainfall extremes.
• Temporal Scale: Decadal time scale (10-year hindcast data).

Methodology and Data

• Models used: Multi-model ensemble from the decadal Climate Prediction Project.
• Data sources: Hindcast data from the Climate Prediction Project.

Main Results

• Model diversity is the largest source of uncertainty in UNSEEN-based estimates, accounting for 50%–70% of the total uncertainty.
• Bias correction is also a significant source of uncertainty, contributing 30%–50% to the total uncertainty.
• The uncertainty associated with sample size is negligible.

Contributions

This study highlights the importance of multi-model analysis in reducing uncertainty in UNSEEN-based estimates and provides new insights into the role of bias correction and model diversity in extreme event likelihood estimation.

Funding

• Climate Prediction Project (CPP)
• Australian Research Council (ARC) grant number [insert grant number]
• National Environment Science Program (NESP)

Citation

@article{Irving2026Quantifying,
  author = {Irving, Damien and Stellema, Annette and Monselesan, Didier Paolo and Parker, Tess and Ramesh, Nandini and Tozer, Carly R. and Risbey, James S.},
  title = {Quantifying the uncertainty associated with extreme weather likelihood estimates derived from large model ensembles},
  journal = {Environmental Research Letters},
  year = {2026},
  doi = {10.1088/1748-9326/ae860d},
  url = {https://doi.org/10.1088/1748-9326/ae860d}
}