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Predictability of extreme events in a nonlinear stochastic-dynamical model
1British Antarctic Survey, Cambridge, United Kingdom. chan1@bas.ac.uk
Summary
Reduced order models accurately capture extreme weather event characteristics and predictability in complex systems. This research shows their potential for modeling and predicting climate extremes.
Area of Science:
- Climate Science
- Dynamical Systems Theory
- Statistical Modeling
Background:
- Complex dynamical systems, such as climate models, often require computationally intensive simulations.
- Understanding and predicting extreme events in these systems is crucial for risk assessment and mitigation.
Purpose of the Study:
- To evaluate the efficacy of reduced order models (ROMs) in replicating extreme event and predictability features of high-dimensional dynamical systems.
- To assess the potential of ROMs for modeling and statistical prediction of weather- and climate-related extremes.
Main Methods:
- Utilized a nonlinear toy model incorporating key characteristics of comprehensive climate models.
- Applied a systematic stochastic mode reduction strategy to derive ROMs.
- Analyzed extreme value characteristics using generalized Pareto distribution and assessed forecast skill via a precursor approach.
Main Results:
- The stochastic mode reduction strategy yielded ROMs with extreme value characteristics consistent with full dynamical models across various time-scale separations.
- Extreme events in the model exhibited a generalized Pareto distribution with a negative shape parameter, indicating boundedness.
- A precursor approach demonstrated good forecast skill for extreme events, and ROMs effectively captured the predictive skill of the full models.
- Confirmed that larger extreme events are more predictable.
Conclusions:
- Systematically derived ROMs show significant potential for modeling and statistical prediction of weather- and climate-related extreme events.
- These findings suggest broader applicability of ROMs in other scientific and engineering domains requiring the analysis of extreme events.
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